"... In PAGE 4: ...1 VoIP Attributes For VoIP, the analog or PCM voice signals are encoded and compressed into a low- rate packet stream by codecs. Table1 lists the attributes of several commonly used codecs. Generally, the codecs generate constant bit-rate audio frames consisting of 40- byte IP/UDP/RTP headers followed by a relatively small payload.... In PAGE 26: ... 8, the interfering uplink TCP_ACK) even when the voice sources are CBR rather than VBR. This can be seen from the results in the first row of Table1 1, in which six VoIP sessions in the M-M scheme coexist with one TCP connection. Tables 9 and 11 both assume six VoIP sessions.... In PAGE 27: ...54 Table 12 shows the results when the number of VoIP sessions is 11, half of the capacity of the M-M scheme when there is only VoIP traffic. Compared with Table1 1, it is clear that TCP just picks up the remaining bandwidth in the WLAN after the VoIP ... ..."

"... In PAGE 5: ... The hardware apos;s task is simpli ed if there is a many-to-one mapping (for example in a direct mapped cache), and could be simpli ed even further if there is a one-to-one mapping. Table1 presents the attributes of primary registers, caches, stack caches[6], and local memories[5]: components that are found in common memory hierarchies. Primary registers, or simply reg- isters, are found in all modern architectures.... ..."

"... In PAGE 9: ... To test this, a series of simulations was run on processor models of different depths. Each model was created by multiplying all of the latencies in Table1 by a constant. Table 2 shows the resulting IPC loss from Baseline scheduling to the two feedback-adjusted prediction schemes.... In PAGE 9: ... Table 2 shows the resulting IPC loss from Baseline scheduling to the two feedback-adjusted prediction schemes. The relative depth in Table 2 is the constant by which the pipeline depths in Table1 were multiplied. Table 2.... ..."

"... In PAGE 23: ... According to Maizell, the number of papers written by and patents issued to a chemist may be a measure of his creativ- ity. In a test of this belief, the number of papers and patents since 1967 by the group of ten chemists who had read at least five of the 59 books published in 1971 was compared with the number of such documents authored by two groups of ten chemists randomly selected from those who used less than five and none of the books, respectively ( Table1 ). Despite Table 1.... In PAGE 28: ...Table1 . SHARP Operating Costs AVERAGE TIME AVERAGE TOTAL SYSTEM (SYST SEC) COST($) RATE($) FREQUENCY COST/YR UPDATE 4800 $300 REPORTING* 609 60 THESAURUS 600 60 TOTAL *Catalog cards, accessions bulletins, worksheets.... In PAGE 28: ... Some spe- cial subject requests are run on a regular monthly basis, and several of the more popular bibliographies, such as the one on quot;underwater sound quot; are updated annually. Operating costs for processing SHARP data in the batch mode on the CDC 6700 are presented in Table1 . Experiments operating the system in the interactive mode using direct access files indicate ... In PAGE 31: ...Table1 . Information Exchange Center Services and Fee Schedule--1973 Copy Service: $1.... ..."

"... In PAGE 9: ... Mapping 1: For each relation R with attributes A1; : : :; An; a predicate rn(A1; : : :; An) is formed, rn being the string of R apos;s name. For the small database in Table2 , we would have two predicates, vehicles(ID; Produced; Licensed) and regions(Place; Region). If we do not know which of the attributes is relevant, we have to write several rule schemata.... In PAGE 9: ... Let i, the number of places, be 2000. Then in our example from Table2 , the hypothesis space has at most 4 (2 20003)2 hypotheses. If we map each attribute of each relation to a predicate, we enlarge the number of predicates, reduce the number of rule schemata, and can easily avoid unconstrained universally quanti ed variables.... In PAGE 10: ...If the primary key of the relation is a single attribute, we get two{place predicates. In our example ( Table2 ), the predicates of the second mapping are vehicles produced(ID; Produced), vehicles licensed(ID; Licensed), and regions region(Place; Region), where ID and Place are the keys of the tables vehicles and regions, respectively. Only 2 rule schemata have to be written in order to learn rules which have a similar { but more speci c { meaning as rules (iv) and (v): mp1(P; Q) : P(Y; X1) ! Q(X1; Z) mp2(P1; P2; P3;Q) : P1(X1; Z) amp; P2(Y; X1) amp; P3(Y; X2) ! Q(X2; Z) The number of predicates is bound by the number of relations times the maximal number of attributes of a relation (without key attributes).... In PAGE 10: ... Mapping 3: For each attribute Ai which is not a primary key and has the values a1; : : :; an a set of predicates rn AI ai(Aj; : : :; Al) are formed, Aj; : : :; Al being the primary key. In our example from Table2 the third mapping delivers these predicates: vehicles produced stuttgart(ID) vehicles produced ulm(ID) : : : vehicles licensed stuttgart(ID) vehicles licensed ulm(ID) : : : regions region europe(Place) regions region asia(Place) : : : Using this representation, rules like (iii) to (v) cannot be learned. Let the number of di erent places be again 2000 and the number of regions be 10; then p is 4010.... ..."

"... In PAGE 4: ... The prepared-to-commit state is visible if the scheduler can decide whether the prepared task should commit or abort. Table1 shows the attributes of the significant events of transactions commonly found in database applications and DBMSs. Therein, a p indicates that the given attribute always holds, whereas a indicates that the given attribute may or may not hold.... ..."