### Table 2: Benchmark abbreviations used here

in Reviewers

"... In PAGE 12: ... It consists of a table of 3-bit saturating counters. The counter values are in- cremented whenever an instruction produces the same value Parameter Value Fetch bandwidth 8 instructions per cycle Functional Units 3 FP, 6 Int (4 load/store) Instruction Queues 32-entry FP, 32-entry Int Inst Cache 64KB, 2-way, 64-byte lines Data Cache 64KB, 2-way, 64-byte lines L2 Cache (on-chip) 2 MB, 4-way, 64-byte lines Latency (to CPU) L2 18 cycles, Memory 150 cycles Pipeline depth 8 stages Min branch penalty 6 cycles Branch predictor 4K gshare Instruction Latency Based on Alpha 21164 Table2 . The processor configuration.... In PAGE 28: ... The lower part shows half-trivial instruction frequency while the upper part shows full-trivial instruction frequency. (See Table2 for benchmark abbreviations) Figure 2: How often each instruction type is trivial. 0% 5% 10% 15% 20% gzp gcc mcf amm prs bzp art swm vpr AVG Half -Trivial Full-Trivial 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% add sub mult div and or xor shift fadd fsub fmult... In PAGE 29: ... In the interest of space, we use the abbreviations shown under the Ab. column in Table2 . We simulated 200M instructions after skipping 200M instructions.... In PAGE 35: ... instr type special format bits ldbu byte zero-extended 8 ldwu word zero-extended 16 ldl long sign-extended 32 ldq, ldqu quad (aligned, unaligned) - 64 lds IEEE single-precision single to double 32 ldt IEEE double-precision - 64 5.3 Benchmarks Table2 describes the twelve C programs from the SPECcpu2000 benchmark suite [1] that we use for our measurements. They were compiled on a DEC Alpha 21264A processor with the DEC C compiler under the OSF/1 v5.... In PAGE 35: ... We further excluded the C++ and Fortran programs due to the lack of a compiler. Table2 shows the number of instructions (in billions) that we skipped before begin- ning the cycle-accurate simulations, the number of simulated load instructions (in millions), the percentage of executed instructions that are loads, and the instruc- tion per cycle (IPC) on the baseline processor. Table 2: Information about the simulated segments.... In PAGE 35: ... Table 2 shows the number of instructions (in billions) that we skipped before begin- ning the cycle-accurate simulations, the number of simulated load instructions (in millions), the percentage of executed instructions that are loads, and the instruc- tion per cycle (IPC) on the baseline processor. Table2 : Information about the simulated segments. skipped simulated % insts (B) loads (M) loads ammp 4.... In PAGE 43: ...VPW2 3 Benchmark Properties We present absolute numbers on the runtime properties of our benchmarks relevant to return value prediction in Table2 . These can be used to suggest specific areas of interest, convert percentages reported in future sec- tions back to absolute numbers, and provide a general feel for dynamic behaviour and characteristics.... In PAGE 43: ...5.8K 56.2M 10.2M 22.9M 43.5M 32.7K 97.5M Table2 : SPEC JVM98 dynamic properties. raytrace is omitted as mtrt is an equivalent multithreaded version.... ..."

### Table 1. Browser Versions

"... In PAGE 2: ... The following four are the most popularly used web browsers in the market today on the windows platform. The abbreviations listed in Table1... In PAGE 6: ... Table 12. Functions to be tested IE OP FF NS Lstrcat Lstrcpy Lstrcpyn Strncpy Wcscat Wcscpy Lstrcpy Lstrcpyn Wcscpy Lstrcat Lstrcpy Lstrcpyn Memcpy Strncpy Lstrcat Lstrcpy Lstrcpyn Memcpy Strncpy From Table1 4, functions that were commonly used by all the browsers were selected. Lstrcpy and Lstrcpyn are two functions that are used by all the four browsers.... ..."

### Table 6 summarizes the measures that we define in detail in the remainder of the section. The measure names are capitalized in association with their abbreviations, which we employ extensively. All of the abbreviations terminate with an R to distinguish these measures from their symmetric counterparts. These measures are appropriate for the request/recommendation scenario pictured in Figure 1; the R stands for Recommendation . They all have some factor associated with either the source or the target concept, which in the symmetric versions of the compatibility measures is associated with both the source and the target.

in Agent Communication with Differentiated Ontologies: eight new measures of description compatibility

1999

"... In PAGE 14: ...links in the ontologies, and one, SHRR (see Table6 ) also considers the structure of the concept definitions. SHRR is surprisingly effective, as we will see in Section 7.... In PAGE 14: ... We include it for theoretical reasons because its derivation directly parallels our calculation of semantic overlap. Table6 : Summary of description compatibility measures None of our compatibility measures exploit concept or relation names that happen to be the same in different ontologies. We give concepts unique names for convenience; because our relations may have the same name, we must always specify a context in order to access them.... ..."

Cited by 9

### Table 1. The methods: abbreviation, reference, sensitivity to i distributions, test statistic and critical level. Method Abbreviation Reference Sensitivity Test statistic Critical level

"... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i ( Table1 : 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i ( Table1 : ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i ( Table1 : 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i ( Table1 : c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i ( Table1 : c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i ( Table1 : c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 ( Table1 , column 3, n=11) c8: Matsumoto and Kubotani (1986: Table 1, column 2, n=8) Subsamples c1, .... In PAGE 1: ...ges, are truncated to the integers (e.g. 66.7Myr to 67, 65 or 70Myr). We studied the in uences of truncation by extending the period analysis of the following samples in Paper i: c1: Paper i (Table 1: 5 ti, n=61) c2: Paper i (Table 1: ti 250, ti 20, D 5, n=34) c3: Paper i (Table 1: 5 ti 300, ti 20, n=35) c4: Paper i (Table 1: c1, ti is not a multiple of 5, n=27) c5: Paper i (Table 1: c2, ti is not a multiple of 5, n=25) c6: Paper i (Table 1: c3, ti is not a multiple of 5, n=23) c7: AM{84 (Table 1, column 3, n=11) c8: Matsumoto and Kubotani (1986: Table1 , column 2, n=8) Subsamples c1, .... In PAGE 1: ...ubsamples c1, ..., c6 are from Table1 of Paper i with n=74 craters of an age ti ti [Myr] and a diameter Di [km]. AM{ 84 detected the 28.... In PAGE 1: ... We applied the Rayleigh (uni{ and bi- modal versions), the Kuiper (1960) and the Swanepoel and De Beer (1990) methods, which are independent from the chosen t = 0. Table1 summarizes their abbreviations and sensitivity to di erent i distributions. Batschelet (1981) dis- cussed the R1{ and K{methods, while Bai (1992) described the R1{ and R2{methods in greater detail.... In PAGE 1: ... We chose =0:001 for a test between Pmin = 10Myr and Pmax = 100Myr. iii) Determine the test statistic for each tested P (see Table1 ). Under H0, solve the probability (i.... In PAGE 1: ...e. the critical level Q, see Table1 ) for the occurrence of this, or an even more extreme value, for the test statistic. iv) Reject H0 only if Q .... ..."

### Table 1 lists the additional axes in TTXPath. Each axis specifies a temporal direction to search for versions (past or future), the desired status of the versions selected (known or both known and assumed), and a tempo- ral order for the resulting node-set. The axis names are rather long, so abbreviations will be handy. Table 2 lists the common abbreviations. The abbreviations favor the transaction-time-past-assumed axis, since we anticipate it will be the most commonly used one. Formally, the transaction-time axes have the following meanings. We assume that DA is the context node, D8D6 is the reference time, D8CR is the current time, and CS is the temporal order. The context is inherited from the environment.

2001

"... In PAGE 10: ...Effect transaction-time-past-assumed select all past versions of the context node and set the temporal text ordering to latest first transaction-time-past-known select only known past versions of the context node and set the temporal text ordering to latest first transaction-time-future-assumed select all future versions of the context node and set the temporal text ordering to earliest first transaction-time-future-known select only known, future versions of the context node and set the temporal text ordering to earliest first Table1 : New axes in TTXPath Axis Abbreviation for tt-known same as transaction-time-past-known tt-assumed same as transaction-time-past-assumed tt-future same as transaction-time-future-assumed tt-past same as transaction-time-past-assumed tt same as transaction-time-past-assumed Table 2: Axis abbreviations in TTXPath tt-pastCJDABN D8D6BN D8CRBN CSCL = CUB4D8BN DAB5CJDABN D8BN D8CRBN D0CPD8CTD7D8CL CY BC AK D8 AK D8D6 CM C0B4D8B5 BP B4 BN BWB5 CM BW BP B4 BN CEBN BXBN B5 CM DA BE CE CV tt-past-knownCJDABN D8D6BN D8CRBN CSCL = CUB4D8BN DAB5CJDABN D8BN D8CRBN D0CPD8CTD7D8CL CY BC AK D8 AK D8D6 CM C0B4D8B5 BP B4CZD2D3DBD2BN BWB5 CM BW BP B4 BN CEBN BXBN B5 CM DA BE CE CV tt-futureCJDABN D8D6BN D8CRBN CSCL = CUB4D8BN DAB5CJDABN D8BN D8CRBN CTCPD6D0CXCTD7D8CL CY D8D6 AK D8 AK D8CR CM C0B4D8B5 BP B4 BN BWB5 CM BW BP B4 BN CEBN BXBN B5 CM DA BE CE CV tt-future-knownCJDABN D8D6BN D8CRBN CSCL = CUB4D8BN DAB5CJDABN D8BN D8CRBN CTCPD6D0CXCTD7D8CL CY D8D6 AK D8 AK D8CR CM C0B4D8B5 BP B4CZD2D3DBD2BN BWB5 CM BW BP B4 BN CEBN BXBN B5 CM DA BE CE CV Examples are given in Section 5.... ..."

Cited by 25

### Table of Abbreviations

2004

Cited by 1