### Table 3: Counter-Arguments For Di erent Views CREDULOUS SKEPTICAL

1998

Cited by 1

### Table 3: Results of Scratch/Non-Scratch Combinations

1991

"... In PAGE 13: ...Table3 shows the results of varying both the number of user-allocable registers and the number of scratch registers for the simple callee, simple hybrid, and smarter hybrid methods. The smarter callee results are not shown since the simple callee results were approximately the same.... ..."

Cited by 21

### Table III. Results of scratch/non-scratch combinations

### Table 1. Summary of methods used in SCRATCH predictors

2005

"... In PAGE 1: ... METHODS The SCRATCH suite combines machine learning methods, evolutionary information in the form of profiles, fragment libraries extracted from the Protein Data Bank (PDB) (1), and energy functions to predict protein structural features and tertiary structures. See Table1 for a summary of the specific methods used by each predictor. The suite includes the following main modules: (i) SSpro (2): three class secondary structure.... ..."

Cited by 9

### Table 1. Summary of methods used in SCRATCH predictors

2005

"... In PAGE 1: ... METHODS The SCRATCH suite combines machine learning methods, evolutionary information in the form of profiles, fragment libraries extracted from the Protein Data Bank (PDB) (1), and energy functions to predict protein structural features and tertiary structures. See Table1 for a summary of the specific methods used by each predictor. The suite includes the following main modules: (i) SSpro (2): three class secondary structure.... ..."

Cited by 9

### Table 2: Multiple runs from scratch

"... In PAGE 13: ... With B15 and B6 we denote the best schedule out of the series of ten. When starting from scratch the ten schedules di er strongly from one another ( Table2 . Routes for long distance tra c only are common to almost all schedules because of the tight time windows further routes cannot be appended.... ..."

### Table 5. Optimizing p0548 from scratch

2001

"... In PAGE 18: ... Table 4 shows the computation times needed to compute a LLL-reduced basis for the lattice generated by the columns of some MIPLIB-problems, after transforming them into the form (1), for different numbers of blocks. To conclude we present Table5 and Figure 2, where all algorithms of this paper were combined. We start with a feasible solution with objective value 13577 that was obtained... ..."

Cited by 6

### Table 23. Optimizing p0548 from scratch

"... In PAGE 39: ...1), for different numbers of blocks. To conclude we present Table23 and Figure 2, where all algorithms of this paper were combined. We start with a feasible solution with objective value 13577 that is obtained by running CPLEX 6.... ..."

### Table 23. Optimizing p0548 from scratch

"... In PAGE 39: ...1), for different numbers of blocks. To conclude we present Table23 and Figure 2, where all algorithms of this paper were combined. We start with a feasible solution with objective value 13577 that is obtained by running CPLEX 6.... ..."

### Table 2: Incremental vs scratch propagation

"... In PAGE 5: ... Emax is obtained by the sum of the maximum values of the number of precedence constraints for each resource and the number of con- straints before the scheduling algorithm starts to find a solution. Table2 and Table 3 present the perfor- Table 1: Number of time points and maximum con- nectivity for the experimental time networks Problem N Emax Cmax = Emax/N P8 130 333 2.56 P10 202 661 3.... ..."