### Table 5: Memoryless Results for Cutting Stock Problems

2003

"... In PAGE 14: ... This meant that the initial matrix entries were left undisturbed throughout the run. The results of these runs are shown in Table5 for the cutting stock problems and Table 6 for the bin packing problems (up to size 4000). Prob HACO No memory avg best time avg best time 6a 79.... ..."

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### Table 5 Memoryless results for cutting stock problems

"... In PAGE 10: ... This meant that the initial matrix entries were left undisturbed throughout the run. The results of these runs are shown in Table5 for the cutting stock problems and Table 6 for the bin packing problems (up to size 4000). It is clear from these results that the local search procedure can solve small problems but needs the ACO procedure when larger problems are encountered.... ..."

### Table 1: References to Slepian-Wolf code designs for different memoryless sources with memoryless correlation.

2003

"... In PAGE 9: ...e., turbo and LDPC codes, the different practical schemes are given in Table1 . References with similar Slepian-Wolf coding setup, i.... In PAGE 10: ...To sum up the recent developments, there is no systematic approach to general practical Slepian- Wolf code design yet, in the sense of being able to account for an arbitrary number of sources with nonbinary alphabets and possibly with memory in the marginal source and/or correlation statistics. None of the approaches in Table1 refer to sources with memory and/or the memory in the correlation between the sources. The only approach taking into account correlation with memory is [34].... ..."

### Table 1 Eigenvalue Spread of the kernel covariance matrix for several memoryless Volterra models and memoryless Fourier models.

"... In PAGE 13: ...y the four models is shown in Fig. 2. This expected behaviour is explained in terms of the eigenvalue spread of the functional covariance matrix of the Fourier and Volterra models. Table1 shows this eigenvalue spread for two model orders N={6, 16}, and for several input distributions (particularly, two uniformly distributed PDFs with ranges [- 1,1] and [-2,2], and three zero-mean Gaussian distributions with standard deviations sX={1/3, 1, 2}). Six models are tested: Volterra (Volt), Odd Volterra (O-Volt), Even Volterra (E-Volt); Fourier (Four), Odd Fourier (O-Four) and Even Fourier (E-Four).... In PAGE 13: ...sX,3sX] for Gaussian distributions, providing an overflow probability of 2.7e-3. The covariance matrices are computed from 5000 data samples and the eigenvalue spread is estimated averaging 10 independent realisations. Thus, Table1 evidences the overall extraordinary advantage of the constant power functionals of the Fourier model with respect to the polynomial functionals of the Volterra model, even in case of dealing with a non-uniformly distributed signal. 5.... In PAGE 13: ...2. Identification of NLSs with memory Table 2 shows the eigenvalue spread of the functional covariance matrix of several Volterra and Fourier models with Q=2, for orders N={5, 10} and for the same input distributions of Table1 . As in the previous subsection, the superiority of the Fourier model is evident.... ..."

### Table 6 Memoryless results for uniform bin packing problems

"... In PAGE 10: ... This meant that the initial matrix entries were left undisturbed throughout the run. The results of these runs are shown in Table 5 for the cutting stock problems and Table6 for the bin packing problems (up to size 4000). It is clear from these results that the local search procedure can solve small problems but needs the ACO procedure when larger problems are encountered.... ..."