### Table 2: Longest Common Subsequence of the First Product

### Table 3: Longest Common Subsequence of the First Product in the Second Cycle and the Third Product in the Second Cycle

"... In PAGE 7: ... Basically, this inter-product correlation is a generalization of inter-cycle correlation. Table3 gives an example which dependences can exist between two different wafer products. In this case 1487 Table 3: Longest Common Subsequence of the First Product in the Second Cycle and the Third Product in the Second Cycle... ..."

### Table 10: Similarity between Machine Extracts and Human Extracts. Mea- sure: longest-common-subsequence.

2003

Cited by 5

### Table 1: True similarity between sequences and the length of longest common subsequence found by using K randomly chosen linear functions; averages over 10 trials. Data: two series of 85 points about the Finnish national economy.

1997

"... In PAGE 3: ... Moreover, it produced approximations to the true similarity that are very close to the correct val- ues. For example, in Table1 we see that for varying , the randomized algorithm got to within 1 from the true optimum already after 500 randomly chosen linear func- tions. Table 2 shows the time needed for this analysis.... ..."

Cited by 25

### Table 1: True similarity between sequences and the length of longest common subsequence found by using K randomly chosen linear functions; averages over 10 trials. Data: two series of 85 points about the Finnish national economy.

1997

"... In PAGE 10: ... Moreover, it produced approximations to the true similarity that are very close to the correct values. For example, in Table1 we see that for varying , the randomized algorithm got to within 1 from the true optimum already after 500 randomly chosen wedges. Table 2 shows the time needed for this analysis.... ..."

Cited by 25

### Table 18: Spearman rank order correlation coefficients of DUC multi- document summarization data between human rankings and some automatic content-based evaluation metrics (in order: cosine, word overlap, bigram overlap, longest common subsequence, and BLEU).

2003

"... In PAGE 22: ... Each human summarizer is also judged by other humans and placed in the ranking. Table18 shows the Spearman rank order coefficients of DUC 2003 and 2004 multi-document summarization data between human rank- ings and different automatic content-based metrics. We also include BLEU [PRWZ01], which is a widely used evaluation metric among the machine... ..."

Cited by 5

### Table 1 contains a summary of the performance of the partitioning strategies in the various cost models. The values are exact only if k divides n exactly and k has an integral square root which divides n exactly. It is clear that although the lines partition fares particularly badly in having a larger number of communications events and delays than either of the other partitions, if k2 gt; (k ? 1)n, it performs, in the time model, the best out of the three partitions. This means that the partition which has, out of the three, the most communication performs the best, and this result holds for any reasonable value of communications delay. The programmer who is faced with the problem of explicitly partitioning a longest common subsequence computation, should therefore make di erent qualitative decisions on the partition, and therefore write di erent programs, according to which cost model best approximates his architecture.

1992

"... In PAGE 9: ...t c d M Relevant Model event amp; delay event delay time Boxes n2=pk 2n(pk ? 1) 2(pk ? 1) n2=pk + 2(pk ? 1) Stripes n2=k n(k ? 1) k ? 1 n2=k + (k ? 1)n=k + (k ? 1) Lines n2=k n2 ? n n ? 1 n2=k + k + (k ? 1) Table1 : Summary of the Costs of the Partitions We have not discussed the relative appropriateness of event, delay and time costing, but in systems such as Distributed Memory MIMD architectures, as long as the locality of the computation can be matched to the locality of the architecture, the available communications bandwidth scales up linearly with the number of processors. In this case it need not cost to have a large number of communications events, nor need it cost if there exist long chains of communications events.... ..."

Cited by 1

### Table 1. The performance ratios of the expansion algorithm and Algorithm BNMAS for four random sequences on binary alphabets. Ratio = |CS|/|LCS|. Our Algorithm Seq Length Expansion

"... In PAGE 4: ... The ratios are the average of 100 sets for each testing condition case. In Table1 , we simulate Algorithm BNMAS in one-way and duplex direction. The one-way direction means that our algorithm only finds the common subsequence from tail to head, and duplex direction returns the longer one of the common subsequences of both sides from head to tail and from tail to head.... ..."

### Table 9: Proportion of cases diagnosed as the common disease in Experiment 4.

"... In PAGE 14: ... The hypothesis, therefore, is that subjects will tend to diag- nose s1,-s2,-s3 as the common disease, early in training, and subsequently tend to diagnose -s1,s2,s3 as the rare disease. Table9 shows the proportion of common-disease diag- noses for the two key training cases, and across all cases. The table also shows the number of instances of each type to which subjects responded.... In PAGE 14: ...013). Table9 also makes it clear that subjects responded differently depending on the symptoms; i.e.... ..."

### Table 1 Latency and throughput performance of common floating-point instructions.

"... In PAGE 13: ... Subsequent rounder cycles are then utilized to produce the special result. Performance Table1 shows the performance of common floating-point instructions. Both the latency (L) of execution and the throughput (T) are listed for HFP and BFP formats.... ..."