### (Table 5.4). The proposal must contain enough background information to be meaningful to a

1999

### Table 1 contains enough information to calculate Sraw(AB), as de ned in Equation 5. If aij is the element in the ith row and jth column, then

"... In PAGE 7: ... This is easiest to understand if the partitioning data is presented as a matrix A of intersections between subsets. Table1 shows an hypothetical example with N = 20 and M = 3. Subject A A1 A2 A3 a+j Subject B B1 8 2 1 11 B2 2 3 0 5 B3 2 1 1 4 ai+ 12 6 2 20 Table 1: Intersections between subsets created by subjects A and B.... In PAGE 7: ... Table 1 shows an hypothetical example with N = 20 and M = 3. Subject A A1 A2 A3 a+j Subject B B1 8 2 1 11 B2 2 3 0 5 B3 2 1 1 4 ai+ 12 6 2 20 Table1 : Intersections between subsets created by subjects A and B. The row and column marginal sums from Table 1 can be used to estimate each subject apos;s probability of assigning an image to a given subset.... In PAGE 7: ... Subject A A1 A2 A3 a+j Subject B B1 8 2 1 11 B2 2 3 0 5 B3 2 1 1 4 ai+ 12 6 2 20 Table 1: Intersections between subsets created by subjects A and B. The row and column marginal sums from Table1 can be used to estimate each subject apos;s probability of assigning an image to a given subset. This \Bayesian quot; expected value for element aij is [14]: EB[aij] = ai+a+j N : (13)... In PAGE 12: ... The data used to construct Table 10 were also analyzed using the B statistic. The results appear in Table1 1, and the summary in Table 5. mean median std.... In PAGE 15: ... Table 9 shows the summary of the B agreement between all human and machine partitionings of the images. The full data is found in Table1 4, where extreme values are highlighted by shading. mean median std.... ..."

### TABLE III COMPRESSION CHARACTERISTICS OF TYPICAL INDOOR LIGHT SIGNAL.WE ESTIMATE THE AMOUNT OF INFORMATION CONTAINED WITHIN THE SIGNAL BY COMPRESSING VARIOUS SIGNAL REPRESENTATIONS WITH THE STANDARD UNIX COMPRESSION UTILITIES.

2002

Cited by 569

### TABLE III COMPRESSION CHARACTERISTICS OF TYPICAL INDOOR LIGHT SIGNAL.WE ESTIMATE THE AMOUNT OF INFORMATION CONTAINED WITHIN THE SIGNAL BY COMPRESSING VARIOUS SIGNAL REPRESENTATIONS WITH THE STANDARD UNIX COMPRESSION UTILITIES.

2002

Cited by 569

### Table 4: Effectiveness analysis for heuristics with critical threshold requirements

1992

"... In PAGE 15: ...ontaining faults. This minimal threshold is referred to as the critical threshold. After the critical level is decided, suspicious statements are thus highlighted by the heuristic. In Table4 , Rows c and b are the critical thresholds: the ratio of the rank of the critical level to the number of ranked levels and the ratio of suspicious statements within and below the critical level to statements involved in the heuristic (i.... In PAGE 15: ... Because H14 would consider results of other heuristics, a precise critical threshold is hard to define for it. In Table4 , Rows a and b for H14 indicate the number of predicate statements in a tested program divided by the number of the executable statements and the number of the statements highlighted by H1, respectively. Row c tells the effectiveness of using H14 to locate faulty predicate statements.... In PAGE 15: ... A unique threshold, which makes the suggested domain reasonably small and consistently contain faults, is highly desirable. In Table4 , critical thresholds for various heuristics in Row b range from 1% to 91%. A standard threshold cannot be decided within this wide scope.... ..."

Cited by 7

### Table 4: Effectiveness analysis for heuristics with critical threshold requirements

1992

"... In PAGE 15: ...ontaining faults. This minimal threshold is referred to as the critical threshold. After the critical level is decided, suspicious statements are thus highlighted by the heuristic. In Table4 , Rows c and b are the critical thresholds: the ratio of the rank of the critical level to the number of ranked levels and the ratio of suspicious statements within and below the critical level to statements involved in the heuristic (i.... In PAGE 15: ... Because H14 would consider results of other heuristics, a precise critical threshold is hard to define for it. In Table4 , Rows a and b for H14 indicate the number of predicate statements in a tested program divided by the number of the executable statements and the number of the statements highlighted by H1, respectively. Row c tells the effectiveness of using H14 to locate faulty predicate statements.... In PAGE 15: ... A unique threshold, which makes the suggested domain reasonably small and consistently contain faults, is highly desirable. In Table4 , critical thresholds for various heuristics in Row b range from 1% to 91%. A standard threshold cannot be decided within this wide scope.... ..."

Cited by 7

### Table 1 contains enough information to calculate S raw n28ABn29n2c as den0cned in Equation 5. If a ij is the element in the ith row and jth columnn2c then

1997

"... In PAGE 7: ... This is easiest to understand if the partitioning data is presented as a matrix A of intersections between subsets. Table1 shows an hypothetical example with N n3d 20 and M n3d 3. Subject A n12 A 1 n12 A 2 n12 A 3 a n2bj Subject B n12 B 1 8 2 1 11 n12 B 2 2 3 0 5 n12 B 3 2 1 1 4 a in2b 12 6 2 20 Table 1n3a Intersections between subsets created by subjects A and B.... In PAGE 7: ... Table 1 shows an hypothetical example with N n3d 20 and M n3d 3. Subject A n12 A 1 n12 A 2 n12 A 3 a n2bj Subject B n12 B 1 8 2 1 11 n12 B 2 2 3 0 5 n12 B 3 2 1 1 4 a in2b 12 6 2 20 Table1 n3a Intersections between subsets created by subjects A and B. The row and column marginal sums from Table 1 can be used to estimate each subjectn27s probability of assigning an image to a given subset.... In PAGE 7: ... Subject A n12 A 1 n12 A 2 n12 A 3 a n2bj Subject B n12 B 1 8 2 1 11 n12 B 2 2 3 0 5 n12 B 3 2 1 1 4 a in2b 12 6 2 20 Table 1n3a Intersections between subsets created by subjects A and B. The row and column marginal sums from Table1 can be used to estimate each subjectn27s probability of assigning an image to a given subset. This n5cBayesiann22 expected value for element a ij is n5b14n5dn3a E B n5ba ij n5d n3d a in2b a n2bj N n3a n2813n29... In PAGE 12: ... The data used to construct Table 10 were also analyzed using the n14 B statistic. The results appear in Table1 1n2c and the summary in Table 5. mean median std.... In PAGE 15: ... Table 9 shows the summary of the n14 B agreement between all human and machine partitionings of the images. The full data is found in Table1 4n2c where extreme values are highlighted by shading. mean median std.... In PAGE 19: ...2749 0.0686 Table1 0n3a Agreement between human subjects on the image partitioning taskn2c as measured by the n14 statistic.... In PAGE 20: ...3825 0.3464 Table1 1n3a Agreement between human subjects on the image partitioning taskn2c as measured by the n14 B statistic.... In PAGE 21: ...1918 n2d0.2522 Table1 2n3a Agreement between computer partitionings of the image setn2c using a variety of factor analysis techniquesn2c number of retained factorsn2c and images featuresn2c as measured by the n14 statistic.... In PAGE 22: ...2058 0.2052 Table1 3n3a Agreement between computer partitionings of the image setn2c using a variety of factor analysis techniquesn2c number of retained factorsn2c and images featuresn2c as measured by the n14 B statistic.... In PAGE 23: ...0914 0.0824 Table1 4n3a Agreement between computer n28X i n29 and human n28S i n29 partitionings of the image setn2c as measured by the n14 B statistic.... ..."

### Table 3: Compression characteristics of typical indoor light signal. We estimate the amount of information contained within the signal by compressing various signal representations with the standard Unix compression utilities.

"... In PAGE 8: ... While this service does not put a burden on the leaf nodes, the routing nodes near the root may need to retransmit the messages from every leaf in the network, roughly two orders of magnitude more. Anecdotal evidence presented in Table3 suggests that this volume of data can be easily reduced by a factor of 2-4 by applying a delta compression and a standard com- pression algorithm (e.... ..."

### Table 2: Signal handling costs

"... In PAGE 15: ...5 Performance Signals such as SIGINT occur infrequently enough that performance is not a major concern, but for concurrency packages that use SIGALRM to provide pre-emption, signal handling overhead is an issue. Table2 contains measurements of the overhead of handling SIGALRM signals in ML. Our... ..."

### Table 1. Array implementation of a sparse matrix HARWELL BOEING TYPE SPARSE MATRIX int nrows number of rows

1999

"... In PAGE 5: ... The value array of the MATRIX object contains the block K for source VECTOR v and destination VECTOR v . We now take advantage of the sparse structure of the blocks K by storing only the nonzero entries in the value eld, while the sparsity pattern is determined by one of several patterns of a form similar to Table1 . The type information in the control eld of the MATRIX object determines which pattern is chosen.... In PAGE 5: ... The type information in the control eld of the MATRIX object determines which pattern is chosen. However, there is one important di erence to Table1 : the double array value in Table 1 is replaced by an integer array offset. These integers are interpreted as o sets in the value-array of the MATRIX object in Table 2.... In PAGE 5: ... The type information in the control eld of the MATRIX object determines which pattern is chosen. However, there is one important di erence to Table 1: the double array value in Table1 is replaced by an integer array offset. These integers are interpreted as o sets in the value-array of the MATRIX object in Table 2.... ..."

Cited by 6