Starting from the most general formulation of stochastic thermodynamics—i.e. a thermodynamically consistent nonautonomous stochastic dynamics describing systems in contact with several reservoirs—, we de€ne a procedure to identify the conservative and the minimal set of nonconservative contributions in the entropy production. Œe former is expressed as the di‚erence between changes caused by tim...

This paper presents an assignment model of CEOs and …rms. The distributions of CEO pay levels and …rms’market values are analyzed as the competitive equilibrium of a matching market where talents, as well as CEO positions, are scarce. It is shown how the observed joint distribution of CEO pay and market value can then be used to infer the economic value of underlying ability di¤erences. The var...

We introduce the rst algorithm for o -policy temporal-di erence learning that is stable with linear function approximation. O policy learning is of interest because it forms the basis for popular reinforcement learning methods such as Q-learning, which has been known to diverge with linear function approximation, and because it is critical to the practical utility of multi-scale, multi-goal, le...

We develop an algorithm for the traveling salesman problem by applying nite diierences to a generating function. This algorithm requires polynomial space. In comparison, a dynamic programming algorithm requires exponential space. Also, the nite-diierence algorithm requires less space than a similar inclusion and exclusion algorithm.

Fractional advection–dispersion equations are used in groundwater hydrology to model the transport of passive tracers carried by uid ow in a porous medium. In this paper we develop practical numerical methods to solve one dimensional fractional advection–dispersion equations with variable coe1cients on a 2nite domain. The practical application of these results is illustrated by modeling a radia...

We present a new connectionist framework for solving deterministic sequential decision problems based on temporal diierence learning and dynamic programming. The framework will be compared to Tesauro's approach of learning a strategy for playing backgammon, a proba-bilistic board game. It will be shown that his approach is not applicable for deterministic games, but simple modiications lead to ...

For the rst interactive Cross Language Evaluation Forum the Maryland team focused on com parison of term for term gloss translation with full machine translation for the document selection task The results show that searchers are able to make relevance judgments with translations from either approach and the machine translation system achieved better e ectiveness than the gloss translation stra...

For the rst interactive Cross-Language Evaluation Forum, the Maryland team focused on comparison of term-for-term gloss translation with full machine translation for the document selection task. The results show that (1) searchers are able to make relevance judgments with translations from either approach, and (2) the machine translation system achieved better e ectiveness than the gloss transl...

The success of reinforcement learning in practical problems depends on the ability to combine function approximation with temporal di erence methods such as value iteration. Experiments in this area have produced mixed results; there have been both notable successes and notable disappointments. Theory has been scarce, mostly due to the difculty of reasoning about function approximators that gen...

Once ReΨ and ImΨ are found from these two coupled di erential equations, the time-dependent probability density |Ψ| for a particle represented by the wave function Ψ can be calculated from |Ψ| = Ψ∗Ψ = (ReΨ) + (ImΨ), where the * denotes the complex conjugate. Our task, then, is to solve Equations (2) and (3) numerically, and this computational approach requires that the partial derivatives be ap...