Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA)

We propose an algorithm for identifying vertices from three dimensional (3D) meshes that are most important for a geometric shape creation. Extracting such a set of vertices from a 3D mesh is important in applications such as digital watermarking, but also as a component of optimization and triangulation. In the first step, the Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA) estimates precisely the local curvature, and most important topological features of mesh geometry. Using the vertex geometric importance ranking, the algorithm traces and extracts a vector of vertices, ordered by decreasing index of importance.Comment: Accepted for publishing and Copyright transfered to Advances in Electrical and Computer Engineering, November 23th 201

Economic forecasting in a changing world

This article explains the basis for a theory of economic forecasting developed over the past decade by the authors. The research has resulted in numerous articles in academic journals, two monographs, Forecasting Economic Time Series, 1998, Cambridge University Press, and Forecasting Nonstationary Economic Time Series, 1999, MIT Press, and three edited volumes, Understanding Economic Forecasts, 2001, MIT Press, A Companion to Economic Forecasting, 2002, Blackwells, and the Oxford Bulletin of Economics and Statistics, 2005. The aim here is to provide an accessible, non-technical, account of the main ideas. The interested reader is referred to the monographs for derivations, simulation evidence, and further empirical illustrations, which in turn reference the original articles and related material, and provide bibliographic perspective

Probing emotional influences on cognitive control: an ALE meta-analysis of cognition emotion interactions

Increasing research documents an integration of cognitive control and affective processes. Despite a surge of interest in investigating the exact nature of this integration, no consensus has been reached on the precise neuroanatomical network involved. Using the Activation Likelihood Estimation meta-analysis method, we examined 43 functional Magnetic Resonance Imaging (fMRI) studies (total number of foci = 332; total number of participants, N =820) from the literature that have reported significant interactions between emotion and cognitive control. Meta-analytic results revealed that concurrent emotion (relative to emotionally neutral trials) consistently increased neural activation during high relative to low cognitive control conditions across studies and paradigms. Specifically, these activations emerged in regions commonly implicated in cognitive control such as the lateral prefrontal cortex (inferior frontal junction, inferior frontal gyrus), the medial prefrontal cortex, and the basal ganglia. In addition, some areas emerged during the interaction contrast that were not present during one of the main effects and included the subgenual anterior cingulate cortex and the precuneus. These data provide new evidence for a network of cognition emotion interaction within a cognitive control setting. The findings are discussed within current theories of cognitive and attentional control

The Lowlands team at TRECVID 2008

In this paper we describe our experiments performed for TRECVID 2008. We participated in the High Level Feature extraction and the Search task. For the High Level Feature extraction task we mainly installed our detection environment. In the Search task we applied our new PRFUBE ranking model together with an estimation method which estimates a vital parameter of the model, the probability of a concept occurring in relevant shots. The PRFUBE model has similarities to the well known Probabilistic Text Information Retrieval methodology and follows the Probability Ranking Principle

Minimax mean estimator for the trine

We explore the question of state estimation for a qubit restricted to the $x$-$z$ plane of the Bloch sphere, with the trine measurement. In our earlier work [H. K. Ng and B.-G. Englert, eprint arXiv:1202.5136[quant-ph] (2012)], similarities between quantum tomography and the tomography of a classical die motivated us to apply a simple modification of the classical estimator for use in the quantum problem. This worked very well. In this article, we adapt a different aspect of the classical estimator to the quantum problem. In particular, we investigate the mean estimator, where the mean is taken with a weight function identical to that in the classical estimator but now with quantum constraints imposed. Among such mean estimators, we choose an optimal one with the smallest worst-case error-the minimax mean estimator-and compare its performance with that of other estimators. Despite the natural generalization of the classical approach, this minimax mean estimator does not work as well as one might expect from the analogous performance in the classical problem. While it outperforms the often-used maximum-likelihood estimator in having a smaller worst-case error, the advantage is not significant enough to justify the more complicated procedure required to construct it. The much simpler adapted estimator introduced in our earlier work is still more effective. Our previous work emphasized the similarities between classical and quantum state estimation; in contrast, this paper highlights how intuition gained from classical problems can sometimes fail in the quantum arena.Comment: 18 pages, 3 figure