### TABLE 1: Classification of some of the common jammed ordered lattices of equisized spheres in two and three dimensions, in which Z denotes the coordination number and O is the packing fraction for the infinite latticea

2001

Cited by 3

### Table 2 The analog of Table 1 for periodic boundary conditions Lattice Locally jammed Collectively jammed Strictly jammed

2002

"... In PAGE 4: ... 2. Table 1 classi es common two and three-dimensional lattice packings into the three jamming categories for hard-wall boundaries (Torquato and Stillinger, 2001), and Table2 does the same for periodic boundary conditions (Donev et al.... ..."

### Table IV. Poisson apos;s ratio along 3 directions in the FCC hard sphere solid at the melting density. Results for both isothermal and adiabatic processes are given along with the free volume predictions. The static lattice results are included with the isothermal quantities. Due to the cubic symmetry of the system p(k) = p(?) for the [100] and [111] directions. p, isothermal

### Table 1: Energy potentials for alphabets HP, HP apos; and HPNX. Lattice Protein Folding is NP-hard 25. A large variety of approximation al- gorithms was therefore developed 15;26;27. Most of these are not fast enough to investigate large ensembles of structures and stochastic optimization techniques (see e.g. 28) are not useful either to study ensemble properties of speci cally folded single chainsj. Hart and Istrail29 recently presented an algorithm for the HP model that guarantee folding within at least 3=8 of the optimum energy. It iThe frequency of Hs is the same as in the HP model, such that a random distribution of the HX subset corresponds exactly to the HP model. janother reasons is given in the next section 2.2

1997

Cited by 12

### Table 7.2: SU(2) coe cients. particularly for large z and modest ~ . The 2 per degree of freedom for the whole t is 1:69. Clearly then there are systematic failures of the data to conform to this surface. Nonetheless, I feel that these are really rather modest and that Figure 7.4 does provide fairly good evidence both for the existence of a nite size scaling surface SQ, and for the e cacy of the polynomial (7.2) as a t to it. Since nite size scaling is only asymptotically true, and since the smallest lattice used here is only 83, it is hardly to be expected that there would be no systematic deviations from a surface. What we nd is that these deviations are only comparable in size to, or somewhat smaller than, the random errors in the data and not much larger (since 2= = 1:69 and this represents the mean square size of the deviations compared to the error bars).

1993

### Table 2 The values of at which simulations were performed on N4 lattices at A = 1:25 , crit: and the height of the plaquette susceptibility peak, max N .

"... In PAGE 8: ... One can see the that our long runs on each of the lattices are indeed very close to the location of the peak. Thus minimal systematic errors are expected from the histogramming extrapolation in the location and the heights of the peaks listed in Table2 . Noting that the increase in the 4-volume, N4, is respectively a factor of 3.... In PAGE 8: ...n the discontinuity P seen in Fig. 5, as can be seen from eq. (5). One can use this exponent to predict the peak heights for the N = 12 and 16 lattices. As can be seen from Table2 , these values, 89 and 163 respectively, compare favorably with the Monte Carlo results. A better determination of these peak heights is computationally very hard due to both the large lattice sizes and the increase in autocorrelations.... ..."

### Table 1. Estimates of critical parameters for continuum percolation with hard-core interactions, and comparison with results from freely overlapping percolation. Superscripts (A) and (U) on the exponent U indicate that these values are determined from equations (3) and (6) respectively. For comparison, recent estimates of U for lattice percolation are U = 1.333i0.002 (Blote et a1 1981), U = 1.342*0.004 (Eschbach et al 1981) and U = 1.33*0.01 (Stanley et a1 1982).

1982

### Table 1: Lattice Diagrams versus Lattice Circuits.

"... In PAGE 6: ... First results were presented in [6]. In Table1 we show the advantages of the proposed Lattice Circuits to previous best results for Lattice Diagrams. The dynamic approached is used for the variable ordering.... ..."

### Table 1: Lattice Diagrams versus Lattice Circuits.

"... In PAGE 6: ... First results were presented in [6]. In Table1 we show the advantages of the proposed Lattice Circuits to previous best results for Lattice Diagrams. The dynamic approached is used for the variable ordering.... ..."