This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We formulate the depth recovery task into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the...

We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least squares estimation. We demonstrate the method on synthetic data and finally separate speech and music in a...

Abstract Auto-regressive extractive summarization approaches determine sentence extraction probability conditioning on previous decisions by maintaining a partial summary representation. Despite its popularity, the framework has two main drawbacks: 1) representation is irresolutely denoted weighted summation of all processed sentences without any filtering, resulting in noisy and degrading effe...

The paper investigates the key factors of export dynamics for a set of Central and Southeast European (CSEE) countries in the context of current economic and financial crisis. In order to model the export dynamics a Global Vector Auto Regressive (GVAR) model is defined. As opposed to models which model each country separately, the GVAR combines all country models in a global model which enables...

I apply the Johansen and Swensen (1999, 2004) method of testing exact rational expectations within the cointegrated VAR (Vector Auto-Regressive) model, to testing the New Keynesian (NK) model. This method permits the testing of rational expectation systems, while allowing for non-stationary data. The NK-model is tested on quarterly U.S. and Euro area time series data. I find that the restrictio...

It is an important issue to study the prediction precision of Particulate Matter 2.5, PM2.5 (28 μg/m3), concentration change. The concentration of PM2.5 is influenced by many factors, and its change is characterized by non-linearity and randomness. This paper establishes a prediction model of PM2.5 concentration change to fit the nonlinear and random trend by combining Auto-Regressive Integrate...