### Table 1. Schematic representation of the algorithms underlying interval constraint solving (left) vs. basic DPLL SAT (right). The close analogy suggests a tight integration into a DPLL-style algo- rithm manipulating large Boolean combinations of arithmetic formulae via a homogeneous treat- ment of Boolean and arithmetic parts.

in Efficient solving of large non-linear arithmetic constraint systems with complex boolean structure

2007

"... In PAGE 8: ... 4. Integrating interval constraint propagation and SAT As can be seen from Table1 , branch-and-prune algorithms based on interval constraint propagation (ICP) with interval splitting and the core algorithm of DPPL SAT solving share a similar structure. This similarity motivates a tighter integration of propositional SAT and arithmetic reasoning than in classical lazy theorem proving.... ..."

Cited by 2

### Table 3 shows a comparison of the run-times of post-pruning algorithms and I-REP in the KRK domain with 10% arti cial noise added. All algorithms used Foil apos;s information gain criterion as a search heuristic. The column Initial Rule Growth refers to the initial growing phase that REP and Grow have in common, while the columns REP and Grow give the results for the pruning phases only. The total run-time of REP (Grow) is the run-time of Initial Rule Growth plus the run-time of REP (Grow). In I-REP both phases are tightly integrated so that only the total value of the run-time can be given.

1997

"... In PAGE 17: ... Table3 : Average Run-Time It is obvious that I-REP is signi cantly faster than the post-pruning algorithms. In fact, it is always faster than REP apos;s and Grow apos;s initial growing phase alone, because I-REP avoids to learn an intermediate over tting theory.... ..."

Cited by 27

### Table 2: Comparison between MCRP and MCExOR

"... In PAGE 4: ... due to the tight integration of the routing function and channel assignment. Table2 summarizes the main differences between both protocols. 3.... ..."

### Table 3: DA classi cation results by using single KSs and di erent types of integration.

1998

"... In PAGE 3: ... These last two strategies only di er for the criterion with which the size of the restricted set is chosen. In one case, referred to as N-best in Table3 , the size of this set is de ned independently from the input. On the contrary, in the second case this size is decided dynamically, and depends on the input: all and only the hypotheses whose probability di erence from the best one is less than a threshold are chosen for rescoring.... In PAGE 3: ....3. Results The integration between SCT and DA trigrams by the three strategies described in 4.1 gave the experimental results reported in Table3 , where the strategy having the best performance results to be the linear combination of the two (log) distributions. This strategy also turns out to be better than the tight integration, where the history is directly considered in the SCTs.... ..."

Cited by 1

### Table 1. Integration results using di erent algorithms and images. RE: Registration Error (RE)[5]. IE: Integration Error(IE1) [8]. IE: Integration Error(IE2) (our method).

"... In PAGE 9: ... The experimental results about 6 ob- jects with total 44 images are presented in Figures 6, 7, and 8 and Table 1. From Figures 6, 7, and 8 and Table1 , it can be seen that our algorithm con- sistently outperforms the algorithm proposed in [8] in the sense that in all cases, the integration error has been reduced and more accurate, smooth and water- tight surfaces have been reconstructed. When the registration error is small, our method produces similar results to the method [8], as demonstrated by Figure 7.... ..."

### Table 1. Task: Problem solving and learning from experience, Method: Combining case-specific and general knowledge.

1996

"... In PAGE 8: ... The other example starts out from the top level CBR with the aim of studying methods that integrate different reasoning strategies, in this case purely case-based reasoning and reasoning from general domain knowledge. In Table1 a set of methods, corresponding to some specific research systems, are grouped into levels of integration according to the quot;tightness quot; of integration. The... ..."

Cited by 12

### Table 3 Comparing tight and non-tight automata

"... In PAGE 10: ... Reachability analysis of the automaton usually reduces runtime, but it does not help in reducing the values of m and n. Table3 compares tight to non-tight Bcurrency1 uchi automata when searching for a simple path. The column labeled St in this table indicates whether each property passes (P), or remains undecided (U).... In PAGE 10: ... All properties in this table are passing properties. The column labeled St has the same meaning as in Table3 ; the column labeled tl, when present, reports the ter- mination length. Tables 5 and 6 show the results of applying different methods when handling multiple fairness conditions.... ..."

### Table 3: Results based on tightness

in Data Transmission Strategies over Networks with Different QoS Levels and All You Can Send Pricing

2005

"... In PAGE 15: ...urves, supplier 4 has more bin usage share as 85.73%. Even though Supplier 1 is cheaper at low bandwidth, supplier 4 is cheaper at high bandwidth and high quality. ======================== Insert Figure 4 here ======================== The results of the effect of tightness are given in Table3 . When tightness increases from 50% to 90% the number of bins used and the total cost increases by 69.... ..."

### Table 3. A tight configuration

"... In PAGE 10: ... 73 Table3 presents a sample of the simulation results for what we termed as a tight configuration input. This refers to the situation where the overall capacity of the trucks deployed is just about enough to meet the overall demand of the stations.... ..."

### Table VII. Complexity of TIGHT CONSEQUENCE.

2000

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