Classical coding schemes that rely on joint typicality (such as Slepian-Wolf coding and channel coding) assume known statistics and rely on asymptotically long sequences. However, in practice the statistics are unknown, and the sequences are of finite length n. In this finite regime, we must allow a non-zero probability of coding error and also pay a penalty by transmitting extra bits. The pena...

This paper investigates the effect of reward-penalty mechanism in a closed-loop supply chain with manufacturer, distributor and third party under technology licensing carbon constraints. There remanufacturing scenarios are developed, namely (1) manufacturer is engaged activities, (2) subcontracts to conduct remanufacturing, (3) undertakes manufacturer's authorization. We apply Stackelberg game ...

We present a conversion from the dependency scheme employed by the Pro3Gres parser to the Stanford scheme, as a further step towards unification of dependency schemes. An evaluation of the conversion shows that it is highly reliable, resulting in less than one percentage point performance penalty on the actual parser output. This supports the suitability of the Stanford scheme as a unifying rep...

penalty clause or liquidated damage which is formerly agreed between parties is legally examined and accepted as a rule. although judge could in some circumstances modify this stipulation. yet its economic analysis which is focused on economic efficiency can be considered as a “major unexplained puzzle in the economic theory of law”. this article compares the major legal systems in the case of ...

We introduce penalty-function-based admission control policies to approximately maximize the expected reward rate in a loss network. These control policies are easy to implement and perform well both in the transient period as well as in steady state. A major advantage of the penalty approach is that it avoids solving the associated dynamic program. However, a disadvantage of this approach is t...

It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and performance indicators. In particular, we introduce a risk-adjusted profitability...

We introduce a new relaxation scheme for structural topology optimization problems with local stress constraints based on a phase-field method. The starting point of the relaxation is a reformulation of the material problem involving linear and 0–1 constraints only. The 0–1 constraints are then relaxed and approximated by a Cahn-Hilliard type penalty in the objective functional, which yields co...

We construct an efficient numerical scheme for solving obstacle problems in divergence form. The numerical method is based on a reformulation of the obstacle in terms of an L1-like penalty on the variational problem. The reformulation is an exact regularizer in the sense that for a large (but finite) penalty parameter, we recover the exact solution. Our formulation is applied to classical ellip...