Polyhedral Computations for the Simple Graph Partitioning Problem

The simple graph partitioning problem is to partition an edge-weighted graph into mutually disjoint subgraphs, each containing no more than b nodes, such that the sum of the weights of all edges in the subgraphs is maximal. In this paper we present a branch-and-cut algorithm for the problem that uses several classes of facet-defining inequalities as cuttingplanes. These are b-tree, clique, cycle with ear, multistar, and S, Tinequalities. Descriptions of the separation procedures that are used for these inequality classes are also given. In order to evaluate the usefulness of the inequalities and the overall performance of the branch-and-cut algorithm several computational experiments are conducted. We present some of the results of these experiments.Branch-and-cut algorithm; Facets; Graph partitioning; Multicuts; Separation procedures

Simulation of multivariate diffusion bridge

We propose simple methods for multivariate diffusion bridge simulation, which plays a fundamental role in simulation-based likelihood and Bayesian inference for stochastic differential equations. By a novel application of classical coupling methods, the new approach generalizes a previously proposed simulation method for one-dimensional bridges to the multi-variate setting. First a method of simulating approximate, but often very accurate, diffusion bridges is proposed. These approximate bridges are used as proposal for easily implementable MCMC algorithms that produce exact diffusion bridges. The new method is much more generally applicable than previous methods. Another advantage is that the new method works well for diffusion bridges in long intervals because the computational complexity of the method is linear in the length of the interval. In a simulation study the new method performs well, and its usefulness is illustrated by an application to Bayesian estimation for the multivariate hyperbolic diffusion model.Comment: arXiv admin note: text overlap with arXiv:1403.176

Kategoribaseret udpegning af grå strækninger i praksis

Paperet baseres på ph.d.-projektet; &quot;Grå strækninger i det åbne land - Udvikling, anvendelse og vurdering af alvorlighedsbaseret metode til udpegning, analyse og udbedring af grå strækninger&quot;, der i perioden august 2003 - august 2006 er gennemført ved Aalborg Universitet i samarbejde med Ringkøbing og Viborg amter.Formålet med projektet har været at udvikle en i praksis anvendelig metode til udpegning, analyse og udbedring af grå strækninger på det overordnede vejnet i det åbne land, som er uheldsteoretisk velfunderet. Samtidig har formålet været at udvikle en metode, hvor uheldenes alvorlighed i henhold til målsætningen for trafiksikkerhedsarbejdet i Danmark indgår på systematisk vis.I paperet fokuseres der på, hvordan udpegning af grå strækninger skal foretages i praksis i henhold til den kategori- og alvorlighedsbaserede udpegningsmetode, der er blevet udviklet i projektet. Samtidig vil metodens anvendelighed og det grå strækningsarbejde generelt bliver drøftet og vurderet i forhold til i hvilket grad, det bør indgå som en integreret del af vejbestyrelsernes stedbundne trafiksikkerhedsarbejde. Denne vurdering foretages på baggrund af konkrete udpegninger og analyser af grå strækninger på de amtslige vejnet i Ringkøbing og Viborg amter.</p

Efficacy of new-generation antidepressants assessed with the Montgomery-Asberg depression rating scale, the gold standard clinician rating scale : a meta-analysis of randomised placebo-controlled trials

It has been claimed that efficacy estimates based on the Hamilton Depression Rating-Scale (HDRS) underestimate antidepressants true treatment effects due to the instrument's poor psychometric properties. The aim of this study is to compare efficacy estimates based on the HDRS with the gold standard procedure, the Montgomery-Asberg Depression Rating-Scale (MADRS)

Vision-based weed identification with farm robots

Robots in agriculture offer new opportunities for real time weed identification and quick removal operations. Weed identification and control remains one of the most challenging task in agriculture, particularly in organic agriculture practices. Considering environmental impacts and food quality, the excess use of chemicals in agriculture for controlling weeds and diseases is decreasing. The cost of herbercides and their field applications must be optimized. As an alternative, a smart weed identification technique followed by the mechanical and thermal weed control can fulfill the organic farmers’ expectations. The smart identification technique works on the concept of ‘shape matching’ and ‘active shape modeling’ of plant and weed leafs. The automated weed detection and control system consists of three major tools. Such as: i) eXcite multispectral camera, ii) LTI image processing library and iii) Hortibot robotic vehicle. The components are combined in Linux interface environment in the eXcite camera associate PC. The laboratory experiments for active shape matching have shown interesting results which will be further enhanced to develop the automated weed detection system. The Hortibot robot will be mounted with the camera unit in the front-end and the mechanical weed remover in the rear-end. The system will be upgraded for intense commercial applications in maize and other row crops

A Note on Limit Theorems for Multivariate Martingales

Multivariate versions of the law of large numbers and the central limit theorem for martingales are given in a generality that is often necessary when studying statistical inference for stochastic process models. To illustrate the usefulness of the results, we consider estimation for a multi-dimensional Gaussian diffusion, where results on consistency and asymptotic normality of the maximum likelihood estimator are obtained in cases that were not covered by previously published limit theorems. The results are also applied to martingales of a different nature, which are typical of the problems occuring in connection with statistical inference for stochastic delay equations

A review of asymptotic theory of estimating functions

Asymptotic statistical theory for estimating functions is reviewed in a generality suitable for stochastic processes. Conditions concerning existence of a consistent estimator, uniqueness, rate of convergence, and the asymptotic distribution are treated separately. Our conditions are not minimal, but can be verified for many interesting stochastic process models. Several examples illustrate the wide applicability of the theory and why the generality is needed