the intrinsic clustering structure accurately. We introduce a new algorithm for the purpose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering

It is widely known that when there are errors with a moving-average root close to −1, a high order augmented autoregression is necessary for unit root tests to have good size, but that information criteria such as the AIC and the BIC tend to select a truncation lag (k) that is very small. We

This paper augments OBDD with conjunctive decomposition to propose a generalization called OBDD[∧]. By imposing reducedness and the finest ∧-decomposition bounded by integer i (∧î-decomposition) on OBDD[∧], we identify a family of canonical languages called ROBDD[∧î], where ROBDD[∧0̂

This paper examines the use of autoregressive distributed lag (ARDL) models for the analysis of long-run relations when the underlying variables are I(1). It shows that after appropriate augmentation of the order of the ARDL model, the OLS estimators of the short-run parameters are p T

them, allowing an animated figure to exhibit a substantial repertoire of expressive behaviors. A combination of radial basis functions and low order polynomials is used to create the interpolation space between example motions. Inverse kinematic constraints are used to augment the interpolations

in the framework. We test our method on the problem of multi-class object segmentation by augmenting the conventional CRF used for object segmentation with higher order potentials defined on image regions. Experiments on challenging data sets show that integration of higher order potentials quantitatively

implemented in which the physical world can be invisibly and seamlessly augmented with electronic tags in order to connect physical objects with virtual representations or computational functionality. We demonstrate the utility of linking physical objects to electronic services and actions that are naturally

Abstract 123 This paper presents a many-core visual computing architecture code named Larrabee, a new software rendering pipeline, a manycore programming model, and performance analysis for several applications. Larrabee uses multiple in-order x86 CPU cores that are augmented by a wide vector

in a generalized form relevant also to problems beyond the compass of the standard second-order conditions for optimality. The new algorithm, the "proximal method of multipliers," is shown to have much the same convergence properties, but with some potential advantages.

in the answers to the queries. More generally, query answers can be augmented with probabilistic guarantees of the validity of the answers. In this paper, we study probabilistic query evaluation based on uncertain data. A classification of queries is made based upon the nature of the result set. For each class