### Table 1: Example of common functions of devices: Same functions are mapped to the same gesture; similar functions may be mapped to the same gesture if this is intuitive and no other function is overloaded.

1997

"... In PAGE 2: ... 1). Further control is ac- cording to Table1 , where one gesture is used for each line. Every gesture is mapped to several similar tasks from di erent devices2, which reduces the number of gestures and makes the dialogue more intuitive.... ..."

Cited by 6

### Table 2 with n = 22. Interpretation of the results For a well chosen polynomial f(x), equations [XY ] and [XY 2] do not give any attack. Similarly, for well chosen polynomials f(x), the number of equations [X2Y ] will be similar to the number obtained with truly random quadratic functions (with no trapdoor). However, this condition on [X2Y ] is more restrictive than the similar condition on [XY 2]. Remark: To see how some equations [X2Y ] or [X2Y + XY 2] can be useful for an attack, see section 8.

1996

Cited by 67

### Table 1: Optimization program for linear lossy compressions

in Abstract

"... In PAGE 5: ... 5. Table1 outlines a simple optimization program to find lossy compressions that minimize a weighted sum of the max-norm residual errors, a0a2a1 and a0a4a3 , in Eq. 5.... In PAGE 6: ... 4.2 Structured Compressions As with lossless compressions, solving the program in Table1 may be intractable due to the size of a0 . There are a48 a29a7a12a25 a0 a25 a17 constraints and a25 a0 a25a18a25 a35 a0 a25 unknown entries in matrix a5 .... In PAGE 6: ... One approach is related to the basis function model proposed in [4], in which we restrict a5 to be functions over some small set of factors (subsets of state variables.) This ensures that the number of unknown parameters in any column of a5 (which we optimize in Table1 ) is 3Assuming a49 a50 is small, the a51a9a49 a50 a51 a52 variables in each a49 a53a55a54a9a56 a57 and a51a9a49 a50 a51 variables in a49 a106... In PAGE 7: ... These techniques are rather involved, so we refer to the cited papers for details. By searching within a restricted set of structured compressions and by exploiting DBN structure it is possible to efficiently solve the optimization program in Table1 . The question of factor selection remains: on what factors should a5 be defined? A version of this question has been tackled in [12, 14] in the context of selecting a basis to approximately solve MDPs.... In PAGE 7: ...1. For further com- pression, we applied the optimization program described in Table1 by setting the weights a5 and a6 to a37 and a15 a53a30a53a30 respectively. The alternating variable technique was iterated a37 a8a7 a30 times, with the best solution chosen from a37 a8a7 random restarts (to mitigate the effects of local op- tima).... ..."

### Table 1: Optimization program for linear lossy compressions

in Abstract

"... In PAGE 5: ... 5. Table1 outlines a simple optimization program to find lossy compressions that minimize a weighted sum of the max-norm residual errors, a0a2a1 and a0a4a3 , in Eq. 5.... In PAGE 6: ... 4.2 Structured Compressions As with lossless compressions, solving the program in Table1 may be intractable due to the size of a0 . There are a48 a29a7a12a25 a0 a25 a17 constraints and a25 a0 a25a18a25 a35 a0 a25 unknown entries in matrix a5 .... In PAGE 6: ... One approach is related to the basis function model proposed in [4], in which we restrict a5 to be functions over some small set of factors (subsets of state variables.) This ensures that the number of unknown parameters in any column of a5 (which we optimize in Table1 ) is 3Assuming a49 a50 is small, the a51a9a49 a50 a51 a52 variables in each a49 a53a55a54a9a56 a57 and a51a9a49 a50 a51 variables in a49 a104... In PAGE 7: ... These techniques are rather involved, so we refer to the cited papers for details. By searching within a restricted set of structured compressions and by exploiting DBN structure it is possible to efficiently solve the optimization program in Table1 . The question of factor selection remains: on what factors should a5 be defined? A version of this question has been tackled in [12, 14] in the context of selecting a basis to approximately solve MDPs.... In PAGE 7: ...1. For further com- pression, we applied the optimization program described in Table1 by setting the weights a5 and a6 to a37 and a15 a53a30a53a30 respectively. The alternating variable technique was iterated a37 a8a7 a30 times, with the best solution chosen from a37 a8a7 random restarts (to mitigate the effects of local op- tima).... ..."

### Table 8 Mean points per season by honeymoon, trapdoor and smooth

2002

"... In PAGE 19: ... The risk analysis software @RISK from Palisade Corporation is used to perform the calculations. Table8 shows 27 results of this exploratory analysis, for three values of each of the three choice variables. The three values for each choice variable are chosen to cover the range of plausible values.... ..."

### TABLE IV SUPPORT FOR C CONSTRUCTS

2007

Cited by 2

### Table 1: Two possible step constructions in Statecharts

"... In PAGE 28: ... The function NextConfig calculates the new state con guration given the old state con guration C and the set of transitions T . The possible constructions of a step in Fig- ure 16 are summarized in Table1 . The con guration at the beginning of the step is de ned by the set fA; Cg, assuming that I = fxg.... In PAGE 28: ...ature of the step construction (i.e., the selection of the transition to put in T is made non- deterministically), there are (in this case) three di erent ways of constructing a step; two constructions yielding di erent results are illustrated in the table. The behavior de ned in construction 1 in Table1 is counterintuitive since transition t4, which should \obviously quot; be triggered by the input event x, is not taken. The semantics of RSML is slightly di erent and enforces a more rigorous causal ordering of the transitions taken within a step.... ..."

### Table 1: Two possible step constructions in Statecharts

"... In PAGE 28: ... The function NextConfig calculates the new state con guration given the old state con guration C and the set of transitions T . The possible constructions of a step in Fig- ure 16 are summarized in Table1 . The con guration at the beginning of the step is de ned by the set fA; Cg, assuming that I = fxg.... In PAGE 28: ...ature of the step construction (i.e., the selection of the transition to put in T is made non- deterministically), there are (in this case) three di erent ways of constructing a step; two constructions yielding di erent results are illustrated in the table. The behavior de ned in construction 1 in Table1 is counterintuitive since transition t4, which should \obviously quot; be triggered by the input event x, is not taken. The semantics of RSML is slightly di erent and enforces a more rigorous causal ordering of the transitions taken within a step.... ..."

### Table 2: Comparison of UDVS Schemes Approximate Computation Time. Here we count the cost of computing a product axbycz as equivalent to a single exponentiation (exp.) in the underlying group. For RSAUDVS exponent lengths are all log2(e). TH denotes the cost of evaluating the trapdoor hash function Fpk (typ. 1 exp.).

in Efficient Extension of Standard Schnorr/RSA signatures into Universal Designated-Verifier Signatures

"... In PAGE 13: ... However, the computation is about the same as in the Schnorr-based schemes. This is because the O(lJ/ log2(e)) exponentiations for RSAUDVS shown in Table2 use a low exponent e, so the total computation is only O(lJ) modular multiplications. Scheme Extended Sig.... ..."

### Table 1 Best achievable compression ratio associated with the optimum achievable region for each image and lossy coder

2001

"... In PAGE 9: ... Note that these performance bounds are operational in that they were achievable by a constructive procedure, as suggested in Theorem 2. Table1 illustrates the results ofthe maximin Cher- noVT procedure for selection of the optimum achiev- able region. Thus this table shows the best achievable ratio associated with the optimum achievable region... In PAGE 13: ...this study. As illustrated in Table1 , the best achiev- able compression ratio is tight for situations of practical relevance. The third step ofthe coder selection procedure consists ofcharacterizing an optimum coder on a Bayesian error probability basis, as suggested by Theorem 3.... ..."