and their interaction within higher order entities. To cope with the pose problem (how to compare 2D projective image features with 3D Euclidean object features), the principle we propose is to reconstruct image features (e.g. points or lines) to one dimensional higher entities

and dimensionality reduction is performed using both the Higher Order Singular Value Decomposition (HOSVD) method and the Kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our

real-world problems, where physical properties of entities exercise inuence on results. Introduction The area of spatial reasoning is fruitful on research work concerning one (Detcher et al. 1991) and two dimensional spaces (Retz-Schmidt 1988). However, in what respects to higher dimensional spaces

Space tensors appear in physics and mechanics, and they are real physical entities. Mathematically, they are tensors in the three-dimensional Euclidean space. In the research of diffusion magnetic resonance imaging, convex optimization problems are formed where higher order positive semi

Abstract—In this paper, the 2-dimensional decentralized parallel interference channel (IC) with 2 transmitter-receiver pairs is modelled as a non-cooperative static game. Each transmitter is assumed to be a fully rational entity with complete information on the game, aiming to maximize its own

describes how tree-ordering techniques can be applied to find orderings for other types of spatial entities: ordering points in the plane, ordering points in higher dimensional spaces, ordering vertices of any graph, ordering edges of any graph, ordering line segments in two-dimensional networks, ordering

of these applications are three dimensional, but some can be extended to higher dimensions. The goal is to distribute the responsibilities of a real time three dimensional game server over multiple servers. By distributing the game server we can split the responsibilities of a single server among many servers

The exponential growth of available computing power opens the door for new capabilities in modeling the objects in the high dimensional real world that we live in. Traditionally we have manipulated data defined on simple entities such as the real line (e.g. audio), a rectangle in the plane (e

that fields do have non-local features, i.e. action at a distance. Others have claimed that topological effects may interpret the effect in which potentials are modeled as connections in higher-dimensional fiber bun-dle geometries. The most recent argument has been proposed by Vaidman [Vai12] who claimed

demonstrate that our method works better than other existing methods. We mainly focus on a 3-dimensional process, but our framework is extendible to higher dimensions. We present two applications of the proposed method in credit risk modelling. Firstly, we show that we can efficiently estimate the default