man raises level of his needs by falsifying the nature and construction of the built spaces. by examining various aspects and complex dimensions of the human, variable and different needs can be clearly seen. therefore, an architectural construction, especially housing, should be able to set the access conditions to meet the highest level of the needs and achieve to the highest level of respons...

Locally weighted learning techniques, in particular LWPR [Vijayakumar et al., 2002], have successfully been used for high-dimensional regression problems. Their robustness and efficient online versions are crucial in robotic domains where, for instance, an inverse model of an articulated dynamic robot has to be learned in real-time. Such models map a highdimensional state (e.g., joint angles an...

Latent variable models are useful tools for statistical data analysis in many applications. Examples of popular models include factor analysis, state-space models and independent component analysis. These types of models can be used for solving the source separation problem in which the latent variables should have a meaningful interpretation and represent the actual sources generating data. So...

Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time. A few highly-structured models, such as the linear dynamical syste...

In this work, we are interested in structure learning for a set of spatially distributed dynamical systems, where individual subsystems are coupled via latent variables and observed through a filter. We represent this model as a directed acyclic graph (DAG) that characterises the unidirectional coupling between subsystems. Standard approaches to structure learning are not applicable in this fra...

Obtaining a global model from the z-variable of the Rössler system is considered to be difficult because of its spiky structure. In this Letter, a 3D global model from the z-variable is derived in a space spanned by the state variable of the time-series itself and generic functions of the other two state variables. We term this space the Ansatz Space. The procedure consists of two steps. First,...

Does the quantum state represent reality or our knowledge of reality? In making this distinction precise, we are led to a novel classification of hidden variable models of quantum theory. We show that representatives of each class can be found among existing constructions for two-dimensional Hilbert spaces. Our approach also provides a fruitful new perspective on arguments for the nonlocality a...

This paper considers an extension of probability space based on interval random variables. In this approach, first, a notion of interval random variable is introduced. Then, based on a family of continuous distribution functions with interval parameters, a concept of probability space of an interval random variable is proposed. Then, the mean and variance of an interval random variable are intr...