"... In PAGE 16: ... The closeness of the set of the conics generated in the second image can be deduced from Figure 3 in which four of them are displayed. Table3 shows the computed for each pair of conics. The absolute value of increases with the increasing discrepancy of conic pairs.... ..."

"... In PAGE 29: ... In Tables 1 and 3, we de ne the reduction of behaviours as a big step semantics. We reformulate the rules in Table3 , following a small step approach: (b02) (f+(~ p); yield:b; ; s) ! (f+(~ p); b; ; s) (b05) (f+(~ p); match r with j p ) b j : : : ; ; s) ! (f +([p=r]~ p); b; 1 ; s) if 1p = s(r) (b06) (f+(~ p); g(~e); ; s) ! (g+(~ q;~rg); b; 1; s) if ~e + ~v; g(~ q) = b; 1~ q = ~v (b07) (f+(~ p); r := e:b; ; s) ! (f+(~ p); b; ; s[v=r]) if e + v Note that there are no rules corresponding to (b1), (b3), (b4) since these rules either terminate or suspend the computation of the thread in the instant. If (f +(~ p); b; ; s) ! (g+(~ q); b0; 0; s0) by the rules above then some register variables r1; : : : ; rk in ~ p can be instan- tiated.... ..."

"... In PAGE 6: ... A reduced description network had no problem learning the contingency across wide gaps. Table3 compares the results presented earlier for a standard net with ten context units and the results for an RD net having six standard context units ( = 0) and four units having identical nonzero , in the range of .... ..."

"... In PAGE 11: ... This is in general quite restrictive, because ambients form a hierarchical structure, in which I/O requests may be arbitrarily deep. Table5 shows an encoding that does not su er these problems; the solution adopted in [7] called the coalescing encoding is behaviourally quite di erent. Both input and output requests along channel p are represented by ambients called p.... In PAGE 11: ...[in p.open p. (q1. . qn). open p. [[P]] ] j open p This complicates however the correctness proof (we would need for instance type systems for linear types). In the encoding of Table5 , coactions guarantee that ex- actly one output ambient enters an input one and, in the simpler input clause above, they control when the input am- bient is opened. 7 Related work and conclusions The introduction of coactions has been inspired by the SOS... ..."

"... In PAGE 6: ... The algorithms are simple and do not require correspondences of a large number of conics or the solution of multivariate polynomial equations. A summary of the results is shown in Table1 . Extensions to these methods which handle over determined sets of equations, obtained due to the presence of more than two coplanar conics, need to be explored.... ..."

"... In PAGE 16: ... Table3 : The computed for each pair of conics Ci and C0 j, Ci is a conic of the rst image and C0 j a conic of the second image. 5.... ..."

"... In PAGE 23: ...d #03 = inf 8 #3E #3C #3E : s 5 #0C #0C #0C #0C #0C #0C #0C 2 6 4 0 1 0 1 s 2 s 5 = p 2 0 s 5 = p 2 0 3 7 5 #17 0 9 #3E = #3E ; = 1: Finally, the possibility of the entries in Table2 where weak infeasibility is not involved, can be demonstrated by a 2-dimensional linear programming problem: Example 5 Let n =2,c2#3C 2 ,K=K #03 =#3C 2 + and A = f#28x 1 ;x 2 #29jx 1 =0g; A ? =f#28s 1 ;s 2 #29js 2 =0g: We see that #28P#29 is strongly feasible if c 1 #3E 0,weakly feasible if c 1 =0and strongly infeasible if c 1 #3C 0. Similarly, #28D#29 is strongly feasible if c 2 #3E 0,weakly feasible if c 2 =0and strongly infeasible if c 2 #3C 0.... In PAGE 27: ... #0F The regularizedprogram CP#28b; c; A; K 0 #29 is dual strongly infeasible if and only if F D = ;. Combining Theorem 8 with Table2 , we see that the regularized conic convex program is in perfect duality: Corollary 7 Assume the same setting as in Theorem 8. Then there holds #0F If d #03 = 1, then the regularized primal CP#28b; c; A; K 0 #29 is either infeasible or unbounded.... ..."