### Table 2: Results for the MIREX 2005 contest for symbolic melodic similarity, ranked according to Average Dynamic Recall. Note *: These runs were executed in M2K environment, and thus the runtime includes evaluation time

2005

Cited by 4

### Table 2. Experiments on the benchmark with the ASTRAL symbolic model checker

"... In PAGE 13: ... For comparison, all of the mutated speci cations were also run using the earlier symbolic model checker that did not use dynamic environment generation. The results presented in Table2 show that using the techniques proposed in this paper the model checker is able to nd a violation in a much shorter time. For all of the tests, the constants min speed and max speed are set to be 15 and 20, respectively, and the constant n track is set to be 2.... In PAGE 13: ... In [DK99b], the model checker without dynamic environment generation failed to complete the search on large instances. For each case in Table2 , results are shown both with dynamic environment generation (labeled as \dynamic on quot;) and without it (labeled as \dynamic o quot;). Each result contains the number of nodes visited in the execution tree, the time taken (measured in seconds), and the result status.... In PAGE 14: ... Experiments on the benchmark with the ASTRAL symbolic model checker A number of observations can be made from these preliminary experiments. First, as shown in Table2 under \dynamic on quot;, violations are found in six out of ten mutations. Among these six mutations, M1, M4, M6 and M10 were killed in the rst run.... ..."

Cited by 1

### Table 2: Dynamical Automaton 1.

"... In PAGE 21: ... (Implemented in Dynamical Automaton 1). We de ne a DA (called DA 1) that recognizes the language of Grammar 2 by the Input Map shown in Table2 . The essence of the DA is a two-element vector, z, corresponding to a position on the Sierpinski triangle.... In PAGE 21: ... Given a compartment and a legal input for that compartment, the change in z that results from reading the input is shown in the \State Change quot; column. If we specify that the DA must start with z = (1/2, 1/2), make state changes according to the rules in Table2 as symbols are read from an input string, and return to z = (1/2, 1/2) (the Final Region) when the last symbol is read, then the computer functions as a recognizer for the language of Grammar 2. To see this intuitively, note that any subsequence of the form \a b c d quot; invokes the identity map on z.... ..."

### Table 2: The number of nonzeros obtained by the static symbolic factorization and the dynamic approach in SuperLU.

1998

"... In PAGE 6: ... We have compared the number of nonzeros obtained by the static approach and the number of nonzeros obtained by SuperLU for these matrices. The results in Table2 show that the over-estimation usually leads to about 30 ? 50% more nonzeros, which is acceptable. The extra nonzeros do imply additional computational cost.... ..."

Cited by 32

### Table 2: The number of nonzeros obtained by the static symbolic factorization and the dynamic approach in SuperLU.

1998

"... In PAGE 6: ... We have compared the number of nonzeros obtained by the static approach and the number of nonzeros obtained by SuperLU for these matrices. The results in Table2 show that the over-estimation usually leads to about 30 ? 50% more nonzeros, which is acceptable. The extra nonzeros do imply additional computational cost.... ..."

Cited by 32

### Table 2: The number of nonzeros obtained by the static symbolic factorization and the dynamic approach in SuperLU.

1998

"... In PAGE 6: ... We have compared the number of nonzeros obtained by the static approach and the number of nonzeros obtained by SuperLU for these matrices. The results in Table2 show that the over-estimation usually leads to about 30{50% more nonzeros, which is acceptable. The extra nonzeros do imply additional computational cost.... ..."

Cited by 32

### Table 4. Run times (in sec.) for constraint symbol specialization benchmarks

2007

"... In PAGE 16: ...028 0.029 Constraint symbol specialization Table4 shows the results of constraint symbol specialization. The columns in the table have the same meaning as in Table 3.... In PAGE 16: ... Note that the the number of dynamic calls is at least 50 % as each non- attened constraint is attened. Table4 includes two benchmarks|zebra2 and manners|not reported in Table 3. The benchmark zebra2 applies an additional round of specialization to the zebra program: the unoptimized entry in zebra2 corresponds to the entry in zebra optimized with the (+ at,+pp) option.... ..."

### Table 1. Entropy results for variation of symbol sizes for different G.722 bit rates (kbps).

2004

"... In PAGE 2: ...izes for different G.722.2 bit rates (kbps). Table1 shows that the entropy in bits per sample decreases as the bit rate of the lossy coder increases for all symbol sizes. This result is reasonable as G.... In PAGE 2: ...ll symbol sizes. This result is reasonable as G.722 is an ADPCM coding scheme and hence residuals will have decreased dynamic range and hence entropy as the bit rate and hence coding accuracy increases. Table1 also shows that the entropy decreases as the symbol size increases. The decreased entropy for 16 bit symbols as opposed to 8 bit symbols indicates correlation may be present between the Most Significant Bytes (MSBs) and Least Significan Bytes (LSBs) of each sample.... In PAGE 2: ... The further decrease in entropy for 32 bit symbols could indicate correlation between adjacent residual samples. In contrast to Table1 , Table 2 shows the entropy in bits per sample for a given symbols size is relatively similar for all bit rates of the lossy coder. This could be explained by the fact that G.... In PAGE 2: ... Hence, the residual signal will follow a similar shape to the original speech signal resulting in a similar dynamic range regardless of bit rate. However, similar to the results of Table1 , Table 2 shows that the entropy decreases as the symbol size increases. This result could be explained using similar reasoning as used to explain the corresponding results of Table 1.... ..."

Cited by 1

### Table 3: Dynamic Results

"... In PAGE 4: ... These results are even better than for the static/ xed method, which was a pleasant surprise. Table3 summarizes the results of the dynamic method. The dynamic nature of the tness function allowed the non chaotic system to iterate for a few gen- erations before converging.... ..."