Ma and Spinrad have shown that every transitive orientation of a chordal comparability graph is the intersection of four linear orders. That is, chordal comparability graphs are comparability graphs of posets of dimension four. Among other uses, this gives an implicit representation of a chordal comparability graph using O(n) integers so that, given two vertices, it can be determined in O(1) ti...

Ma and Spinrad have shown that every transitive orientation of a chordal comparability graph is the intersection of four linear orders. That is, chordal comparability graphs are comparability graphs of posets of dimension four. Among other uses, this gives an implicit representation of a chordal comparability graph using O(n) integers so that, given two vertices, it can be determined inO(1) tim...

Most typical statistical and machine learning approaches to time series modeling optimize a singlestep prediction error. In multiple-step simulation, the learned model is iteratively applied, feeding through the previous output as its new input. Any such predictor however, inevitably introduces errors, and these compounding errors change the input distribution for future prediction steps, break...

We study the fitting of time series models via minimization of a multi-step ahead forecast error criterion that is based on the asymptotic average of squared forecast errors. As in Haywood and Tunnicliffe-Wilson (1997) our score function is formulated in the frequency domain, but our time series models are not limited to those with spectra linear in the parameters; our formulation includes all ...

Modeling Web services is a major step towards their automated analysis. One of the important parameters in this modeling, for the majority of Web services, is the time. A Web service can be presented by its behavior which can be described by a business protocol representing the possible sequences of message exchanges. To the best of the authors’ knowledge, automated analysis of timed Web servic...

Abstract. In this paper we propose a new parareal algorithm for parallelizing in time molecular dynamics problems. The original structure of this algorithm allows one to consider multi-time stepping, namely two levels of temporal discretization, providing a larger range for the fine and coarse solvers definition. We also prove the symplecticity of this method, which is an expected behavior of t...

Multi-step prediction is a difficult task ¡hat has been attracted increasing tbe inieres! in recen! years. It tries to achieve predictions several sleps ahead ¡nto the tuture starting from information al time k. This paper is facllsed on the dcvelopment oí nonlinear neural models with tbe purpose oí building long-teTm Uf multi-step time series prediction schemes. In these context, the mos! popu...

Introduction: The neural substrates of temporal processing are not still fully known. The majority of interval timing studies have dealt with this subject in the context of “Explicit timing” (computing the time intervals explicitly). The hypothesis “Implicit timing” (implicitly using temporal processing to improve function) has also proposed. This lesion study addressed explicit and implicit ti...

In this paper, we design, analyze, and numerically validate positive energy-dissipating schemes for solving the time-dependent multi-dimensional system of Poisson–Nernst–Planck equations, which has found much use in modeling biological membrane channels semiconductor devices. The semi-implicit time discretization based on a reformulation gives well-posed elliptic system, is shown to preserve so...