### Table 1: Pattern-finding by simplicity: A sample of research

2003

"... In PAGE 4: ... Simplicity as a cognitive principle So simplicity appears to go some way towards meeting criterion (i): justifying why patterns should be chosen according to simplicity. What about criterion (ii)? Does simplicity explain empirical data in cognitive science? Table1 describes a range of models of cognitive phenomena, from low and high level visual perception, language processing, memory, similarity judgements, and mental processes in explicit scientific inference. The breadth of domains in which simplicity has proved to be a powerful organizing principle in cognitive modelling is encouraging.... In PAGE 8: ... Table1 : Many pattern-finding problems have been successfully approached by mathematicians and computer scientists using a simplicity principle. In many of these areas, the simplicity principle has also been used as a starting point for modelling... ..."

Cited by 15

### Table 5. Hit Miss

1996

"... In PAGE 14: ... Table5 : Match percentages from 351 trials. Scaled Euclidean normalised, matching by correlation of the images.... In PAGE 20: ... Hair has been excluded from the match. and thus to seek the analogue for shape-free faces of Table5 . Because our normalisation methods use a relatively small number of points, the quality of the match between images may be underestimated; to compensate, the correlation between a probe and each gallery image was optimized separately by choosing the scaled Euclidean transform (assumed al- ready very close to the identity) which maximized the image correlation.... ..."

Cited by 13

### Table 3 Effect of Simplicity of Offer Description on Take-Upa

2006

"... In PAGE 15: ... Because of clerical error, the call center did not follow the random list we had created but instead called an arbitrary set of clients. As Table3 in the Appendix indicates, we cannot find strong systematic differences on observables between the customers the call center attempted to call and those that it did not. However, these results should be interpreted more carefully as they may not be causal.... In PAGE 17: ... The magnitude indicates that a 1 percentage point drop in the offer interest rate increases take-up by about .26 percentage points (see column 1 of Table3 ). Given the average take-up rate in the experiment, this implies that a one percentage point drop in the offer interest rate leads to about a 3.... In PAGE 17: ...nterest rate leads to about a 3.5 percent rise in take-up. 4.2 The Description of the Offer Table3 reports the impact of presenting on the offer letter a table with many choices compared to a table with only one choice. How is the sensitivity of take-up affected by this description of the offer? In column 1, the estimated coefficient on the small table dummy is positive and statistically significant.... In PAGE 20: ... 30As we already indicated above, we do not find systematic differences on observable characteristics for the cus- tomers that the call center attempted to call. See Table3 in the Appendix.... ..."

Cited by 1

### Table 6 : Test results for the hill-climbers As can be seen, the random sampling does worst, despite the high sampling rate for F4, F5, F6 and F7. Overall, the hill-climbers out-perform the GAs, with DRHC2 giving the best performance. This algorithm does extremely well over this test set, with an average hit ratio of 0.8. It should be pointed out that these results show only hits on the global optimum, and do not show, for example, if an algorithm very quickly reached a good sub- optimum, but failed to reach the global one. Perhaps most surprising is the effectiveness and computational simplicity of the hill-climbers. Most

1995

Cited by 16

### Table 2: Cache Hit Trace

"... In PAGE 6: ... A REG is the set f i j 8xi 2 Sg. Example 1 Consider the trace of hits (H) and misses (M) from a cache in Table2 . The state space S for this trace is given by fH; Tg.... In PAGE 7: ...alue. The rate of occurrence of a state xi at event number t is the slope of the REG path, i, at t. Consequently the event number 0 along with any event number at which the slope of any REG path changes signi cantly from its previous value is a partitioning event. Example 2 Figure 4 shows the set of partitioning events for the example trace in Table2 (see details in Section 5). The rate of occurrence of the state Hits changes at event numbers 0; 4; 8; 12; 13; 16.... In PAGE 7: ... De nition 3 Consider an event t 2 R. The rate vector t is de ned as the set t = f i;t j xi 2 Rg: Example 3 For the example trace in Table2 the rate vectors t, for all t in R, are displayed in Table 3.... In PAGE 8: ... The set estimates the joint probability mass function fpXt xt j t 2 Rg. Example 4 For the example trace in Table2 , the estimated joint probability mass function fpXt xt jt 2 Rg is displayed in Table 4. 5 Estimation of Partitioning Events In this section we describe the computation of the set R.... In PAGE 9: ... Let us denote the collection of all state partitioning events for xi as Ri. De nition 7 For a state xi, and its corresponding REG path i, the set of state partitioning events, Ri, is given by: Ri = Bi [ Ai: Example 5 For the example trace in Table2 , the sets B0 and B1 are given by f0; 8; 13g and f4; 12g respectively. The sets E0 and E1 are given by f3; 11; 15g and f7; 16g respectively.... In PAGE 10: ... We compute the rate i;ta1 as the slope parameter of the linear regression function [Ott88] that ts the points (ta1; g(xi; ta1)); : : : ; (ta2 ? 1; g(xi; ta2 ? 1)). Example 6 For the example trace in Table2 , the rates 0;t (t 2 R0) are shown in Table 5. Similarly, the rates 1;t (t 2 R1) are shown in Table 6.... In PAGE 10: ... Let i denote the subset of such that the members of i are the rate vectors t corresponding to the state partitioning events in Ri. Formally, i = f t j 8t 2 Rig: Example 7 For the example trace in Table2 the sets 0 and 1 are displayed in Table 7 and Table 8 respectively. The set can now be computed as a union of all i.... In PAGE 10: ... The set can now be computed as a union of all i. Formally, = Sxi2S i: Example 8 For the example trace in Table2 the set is displayed in Table 3. Finally, each rate vector t in the set is normalized as described below.... In PAGE 10: ... The set of rate vectors can now be used as an estimate of the joint probability mass function fpXt xt j t 2 Rg. Example 9 For the example in Table2 the estimated joint probability mass function is displayed in Table 4. 7 Model Storage We store the model as the sets Ri and f i;t j t 2 Rig for each state xi in the trace.... In PAGE 11: ... The storage for the model is therefore given by: C m X i=1 ki (4) Constant C denotes the constant storage needed for each hpartitioning event, ratei pair. Example 10 For the example trace in Table2 our model is stored as Tables 5 and 6. According to equation (4) above the storage requirement for our model is (5 + 6)C = 11C units.... ..."

### Table 3: Number and type of breakpoints hit

"... In PAGE 8: ... The output from each benchmark run for tdb and gdb was also automatically compared. Table3 shows the distribution of the breakpoints that were hit for the benchmarks. The table shows the number of unique break- points that were hit for the different types of translations.... ..."

### Table 7: Hits by implementation, with associated ratings

"... In PAGE 4: ... Still, I believe that they have proven useful in pointing people to the most relevant implementation. 3 Table7 records the number of hits received for each implementation, along with the problem for which it received the highest rating, as well its average rating across all problems. LEDA [2] received almost as many hits (2084) as the two following implementations, both associated with popular books [4] (1258) and [1] (994).... ..."

### Table 4. Table 3 :Qrecall and Hit-rate in an optimum prediction

"... In PAGE 7: ... Table4 : Characteristics of Qrecall and Hit-rate. Parameter Hit-rate Qrecall Function increasing/decreasing Non monotonic Monotonically increasing End point Proportion of positive samples in the set 1 Sensitivity of the measures value to positive instances Very sensitive to positive instances at the top of the list.... ..."

### Table 2. For simplicity and brevity, we will use the state space from the first row of Table 2, shown in Figure 6, to explain the dynamics of the detailed design model.

"... In PAGE 4: ... When the target is initially further away from the missile, then the simulator will take more iterations before a hit occurs than if the target was closer. Table2 shows some state space results for different configurations. Parameter Values State Space Results Tol.... In PAGE 4: ...005 (1,0,0) (103, 103, 103) 14509 23400 468 0.01 (1,0,0) (103,0,0) 5457 8800 176 Table2 : Detailed Design State Space Results. Tol = tolerance, TS = terminal states, Units: km and hours.... ..."