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TABLE NON-TRIVIAL COMMUTATORS:

in THIN GROUPS OF PRIME-POWER ORDER AND THIN LIE ALGEBRAS
by M. F. Newman, C. M. Scoppolaf 1994

Table 2. Non-trivial infrastructure requirements.

in A Specification for an Object-Oriented Distributed Banking System
by Sy St Em, A Specification For An Object-oriented
"... In PAGE 19: ... Once the interfaces have been identified, the detailed form of the interface opera- tions can be derived from the data contents of the objects given in the informational specifica- tion. Finally, Table2 contains a list of non-trivial requirements that the selected middleware Computational object Functionality Account repository Maintenance of accounts and account agreements for a branch. Account type Maintenance of an account agreement template.... ..."

Table 1: Non-trivial algorithm settings.

in Comparison of Face Matching Techniques under Pose Variation
by B. Kroon 2007
Cited by 1

Table 1 and 5, the performance gap between the Statistical/Worst-Case and Deterministic/Worst- Case algorithms indeed widens, although not as signi cantly as expected. This seems to indicate that non-disk-related overheads that exist independently of the access patterns of the workload play a non-trivial role in the overall resource consumption. Similar conclusions can be drawn when comparing Table 2 and 6.

in An Empirical Study of Admission Control Strategies in Video Servers
by Michael Vernick, Tzi-cker Chiueh
"... In PAGE 12: ... An I/O cycle can not service all of the streams if the total time to service the streams is greater than the cycle time. For example, as shown in Table1 when running the No Prediction algorithm on six disks, a maximum of 42 streams were admitted into the system. But when running 42 streams, 23% of the cycles caused an overload.... In PAGE 16: ...8%) 63(6%) j 64(27%) 66(54%) Table 7: Admission control performance for a cycle time of 1 second, using a uniformly distributed workload with low bit rate videos Disks Deterministic Deterministic Statistical Statistical No Worst-Case Average Worst-Case Average Prediction 2 46 59 66 70(7%) 69(2%) 4 65 70 83 85(5%) j 86(10%) 86(14%) 6 74 82 98 102(2%) j 103(24%) 102(2%) Table 8: Admission control performance for a cycle time of 2 second, using a uniformly distributed workload with low bit rates Since the playback rates of the videos are lowered, more streams can be admitted than in the previous experiments. Comparing Table 7 with Table1 the deterministic algorithms do not allow as many (percentage of maximum) streams when using a lower bit rate. For example, when using six disks and a playback rate of 180KBps, the deterministic worst-case algorithm allows 31 streams or 76% of the maximum number of streams allowed by the no prediction algorithm.... ..."

Table 1. Non-trivial equations found for basic HFE

in The security of Hidden Field Equations (HFE
by Nicolas T. Courtois 2001
Cited by 18

Table 2: Data on 5008 Non-trivial Dialogues from

in Mechanisms for Mixed-Initiative Human-Computer Collaborative Discourse
by Curry I. Guinn 1996
Cited by 18

Table 1. Non-trivial equations found for basic HFE

in The security of Hidden Field Equations (HFE)
by Nicolas T. Courtois 2001
Cited by 18

Table 1.1. Non-trivial irreducible isomorphism correspondences

in ICHIRO SHIMADA
by unknown authors

Table 1. Bar index table for Yellow Submarine The number of extracted repeating patterns was found to vary across different songs. Some songs tended to have more repeating patterns than others. The length of repeating patterns also varied across songs. The number of segmented bars of a piece of music is dependent on the length of the input music. However, the bar index table of a piece of music will be dependent on the nature of the piece of music. In our system, we only set the minimum length threshold since very short repeating patterns were not deemed to be important. Some non-trivial (i.e., lengthy) repeating patterns may be extracted from a music piece. In fact, a repeating pattern in a full-length repeating pattern may or may not sound as a complete repeating melody, since only certain combinations of notes can be used to end a melody. Therefore, the proposed system is used only to extract full-length repeating patterns. Table 2 shows the statistical information of the tested songs. As seen in Table 2, some repeating patterns end up being very long after pruning. For example, Hotel California by Eagle has only two very long repeating patterns that are truly the two main melodies as confirmed by informal listening tests.

in Music indexing with extracted main melody by using modified lempel-ziv algorithm
by Hsuan-huei Shih, Shrikanth S. Narayanan, C. -c. Jay Kuo 2001
"... In PAGE 10: ... Then, the decomposed numerical music score is used to build a bar index table and converted into bar indexed music score based on the bar index table. Table1 shows the entire bar index table for Yellow Submarine. Figure 10.... ..."
Cited by 2

Table 1. Some non-trivial instances decided by preliminary QBF reasoning

in Propositional Skolemization and Symbolic Reasoning
by Marco Benedetti 2004
"... In PAGE 37: ...everal non-trivial formulas (w.r.t. their size) exist in the test benchmarks that are not beyond the deductive power of the incomplete set of rules adopted. Table1 shows a few instances from the QBF library that are completely solved during Step 1. Some sparing monster instances having more than one million variables (such as the biggest ones in the ripple-carry series) were already noticed to be addressable despite their huge size [QBF03].... ..."
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