### Table 1 Sets of 20 problems (maximization)

2005

"... In PAGE 8: ... The chosen bound is always the tighter of the two possibilities. As can be seen from Table1 good results, in terms of speed and quality, are obtained. For the smallest problems solutions were close to optimal and obtained rapidly.... ..."

### Table 2: Results for 30 di erent test problems.

"... In PAGE 15: ... i = 0; Stop := False; repeatfold B := fB, fold I := fI; do x 2 Neighbors(xi) evaluate x ! f(x); if f(x) gt; fB and x is feasible thenfB := f(x), xB := x; elseif f(x) gt; max(fB; fI) thenfI := f(x), xI := x; enddo if fB gt; fold B then xi+1 = xB; elseif fI gt; max(fold B ; fold I ) then xi+1 = xI; else Stop := True; i := i + 1; until Stop = True; if fB gt; 0 then Feasible Local Optimum found else No feasible solution found end.From the 30 test cases that are listed in Table2 , the restart from the best infeasible neighbor turned out to be helpful in almost half of the cases, as is shown in Table 1. On the other hand it shows that in a few cases it caused a longer search without improving the objective function.... In PAGE 16: ...04667 54 1.04670 62 Table 1: Test problems from Table2 where infeasible neighbors were explored. lowed is then gradually decreased, until only feasible patterns can be selected as new parents.... In PAGE 19: ... PI: Pairwise interchange from an arbitrary starting point. The results are listed in Table2 . Computation times are in seconds on an HP 9000/720 workstation.... ..."

### Table 2. Size and performance statistics for learning to distinguish vulnerability from productivity cases.

"... In PAGE 9: ... From there, we see the target and its modes, individuals and their capabilities, and visit and communication events. Columns two and three of Table2 show the number of vertices and edges in all the cases for each dataset. We used Subdue in supervised learning mode to perform a 3-fold cross-validation experiment on each dataset, where the vulnerability case graphs comprised the positive examples, and the productivity case graphs comprised the negative examples.... In PAGE 9: ... For example, this could represent a terrorist driving a truck to the target. The last two columns of Table2 show the cross-validation accuracy and the average learning time per fold for each dataset. Note that the testing phase of the cross-validation experiment involves a subgraph isomorphism to determine if the learned pattern is in the test examples.... ..."

### Table 3. A training example for learning by observation from the Blocks World.

2006

"... In PAGE 3: ... The solution is a sequence of primitive skill instances provided by the expert that achieves the goal starting from the initial state. Table3 shows a train- ing example for a simple problem in the Blocks World domain. The initial state for this problem has a three- block tower with C on B and B on A, and the goal is to get block A clear.... ..."

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### Table 3. A training example for learning by observation from the Blocks World.

2006

"... In PAGE 3: ... The solution is a sequence of primitive skill instances provided by the expert that achieves the goal starting from the initial state. Table3 shows a train- ing example for a simple problem in the Blocks World domain. The initial state for this problem has a three- block tower with C on B and B on A, and the goal is to get block A clear.... ..."

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### Table 5. Post-translational Modifications of R. palustris Ribosomal Proteins

2004

"... In PAGE 11: ... coli S12 ribosomal protein.58 Although some of our data suggest that other ribosomal proteins might possess PTMs, those reported in Table5 include only cases for which supporting evidence from two or more different separation or MS approaches were found. For ex- ample, not included in the robust PTM assignments listed in Table 5 are several modified proteins identified from top-down data only (see Table 3).... In PAGE 11: ...t D88 of the E. coli S12 ribosomal protein.58 Although some of our data suggest that other ribosomal proteins might possess PTMs, those reported in Table 5 include only cases for which supporting evidence from two or more different separation or MS approaches were found. For ex- ample, not included in the robust PTM assignments listed in Table5 are several modified proteins identified from top-down data only (see Table 3). These include L5, L17, L24, S4, S8, S11, and S18.... ..."

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### Table 2. Distinguishing axioms

"... In PAGE 4: ... 4 Axioms This section introduces a lattice of axioms characterising the above weak bisim- ulations. For 2 f ; S; 0; g, the axioms for apos; are given in Table 1, plus the axiom ( ) from Table2 . The axioms for apos; are given in Table 1, plus the ax- ioms ( ) and ( ) from Table 2.... In PAGE 4: ... For 2 f ; S; 0; g, the axioms for apos; are given in Table 1, plus the axiom ( ) from Table 2. The axioms for apos; are given in Table 1, plus the ax- ioms ( ) and ( ) from Table2 . We write E = F if E = F can be derived by application of the axioms for apos; .... In PAGE 4: ... (rec6) states the redundancy of recursion on an unguarded variable in the context of divergence. We discuss the distinguishing axioms in reverse order relative to how they are listed in Table2 . Axiom ( ) characterises the property of WB that divergence cannot be distinguished when terminating.... ..."

### Table 3: Solution of a constraint satisfaction problem by branching, domain reduction, relaxation, and cutting plane generation.

"... In PAGE 7: ... Because the relaxation is distinguished from the model, both are more succinct. A search tree appears in Table3 . At each node constraint propagation is rst applied to the... ..."

### Table 1: Numberofiterations required to nd an optimal solution using the previous solution as a starting

2001

"... In PAGE 24: ... When a new user enters the system, running the MFVA algorithm using as astarting point the optimal solution for the problem prior to the new user apos;s arrival typically results in substantial computational savings. Table1 shows results from a power control problem involving a system of ten bytencells and approximately nine hundred mobile users. The number of iterations required to nd the optimal solution for an initial problem is given, along with the numberofiterations required to nd the optimal solution when additional users enter the system.... ..."

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