DeepMatching: Hierarchical Deformable Dense Matching

We introduce a novel matching algorithm, called DeepMatching, to compute dense correspondences between images. DeepMatching relies on a hierarchical, multi-layer, correlational architecture designed for matching images and was inspired by deep convolutional approaches. The proposed matching algorithm can handle non-rigid deformations and repetitive textures and efficiently determines dense correspondences in the presence of significant changes between images. We evaluate the performance of DeepMatching, in comparison with state-of-the-art matching algorithms, on the Mikolajczyk (Mikolajczyk et al 2005), the MPI-Sintel (Butler et al 2012) and the Kitti (Geiger et al 2013) datasets. DeepMatching outperforms the state-of-the-art algorithms and shows excellent results in particular for repetitive textures.We also propose a method for estimating optical flow, called DeepFlow, by integrating DeepMatching in the large displacement optical flow (LDOF) approach of Brox and Malik (2011). Compared to existing matching algorithms, additional robustness to large displacements and complex motion is obtained thanks to our matching approach. DeepFlow obtains competitive performance on public benchmarks for optical flow estimation

A DECOMPOSITION ANALYSIS OF BASE METAL PRICES: COMPARING THE EFFECT OF DETRENDING METHODS ON TREND IDENTIFICATION AND CYCLICAL COMPONENTS

This thesis analyzes the long-term trend behavior and cyclical components of real base metal prices. A decomposition approach is used to separate the real prices of base metals into a time trend and cyclical components. In this regard, linear and quadratic detrending methods along with Hodrick-Prescott and Baxter-King filters are applied to the base metal prices. Linear and quadratic trendlines are good estimates of the trend component and easy to interpret if the coefficients of estimated trends are significant and the coefficient of determination is relatively high. The Hodrick-Prescott and Baxter-King filters are good fits to the price series, but they affect the cyclical component. Loss of data points and altering the moments of the cyclical component are the disadvantages of filtering methods. On the other hand, the linear detrending methods are weak in removing unit roots in the series. This study shows that the choice of detrending method affects the cyclical component of base metal prices, and consequently the identification of cycles depends on the detrending method

Technical Notes on Volume Averaging in Porous Media I: How to Choose a Spatial Averaging Operator for Periodic and Quasiperiodic Structures.

This paper is a first of a series aiming at revisiting technical aspects of the volume averaging theory. Here, we discuss the choice of the spatial averaging operator for periodic and quasiperiodic structures. We show that spatial averaging must be defined in terms of a convolution and analyze the properties of a variety of kernels, with a particular focus on the smoothness of average fields, the ability to attenuate geometrical fluctuations, Taylor series expansions, averaging of periodic fields and resilience to perturbations of periodicity. We conclude with a set of recommendations regarding kernels to use in the volume averaging theory

A Re-assessment of Credit Development in European Transition Economies

The aim of the paper is to re-assess the bank credit development in 11 Central and Eastern European countries and to provide new estimates of the credit-to-GDP ratio equilibrium level. Using filtering methods and dynamic panel estimations, our results suggest an “excessive” credit development for most of the studied economies until 2007. After this period, while credit has continued to remain excessive in Bulgaria, Hungary, Poland and Slovakia, it has decelerated in the other countries. However, while the results suggest a possibility of “credit crunch” in the Baltic republics and, to a less extent, in Croatia, credit deceleration may lead to “soft landing” for the Czech Republic, Romania and Slovenia.Bank Credit, Dynamic Panel, CEECs

Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation

Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment, has been a subject of growing research in the past decades. Here, we introduce a unified framework that ties together the problems of downscaling, data fusion and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing proper regularization, which are constraints consistent with the degree of smoothness and probabilistic structure of the underlying state. We review relevant regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on hydrologic and atmospheric applications. Informed by the statistical characteristics of the state variable of interest, the central results of the paper suggest that proper regularization can lead to a more accurate and stable recovery of the true state and hence more skillful forecasts. In particular, using the Tikhonov and Huber regularization in the derivative space, the promise of the proposed framework is demonstrated in static downscaling and fusion of synthetic multi-sensor precipitation data, while a data assimilation numerical experiment is presented using the heat equation in a variational setting

Dating and Exploration of the Business Cycle in Iceland

The paper explores the quarterly sequence of business cycles in Iceland for 40 years between 1970 and 2009 using the business cycle technique of Leamer (2009). We apply first a turning point (TP) dating identification procedure based on the Hendrick- Prescott (HP) filter of the quarterly growth rates of GDP and then we use different candidates for leading indicators for turning points. We find that the Iceland economy has a rather short business cycle of about 3 years and most macroeconomic indicators are in accordance with the business cycles. Only a few indicators have a predictive potential, some variables like consumption show a one quarter lag. Furthermore, we apply the concept of abnormal contributions to growth for candidates as a leading indicator of turning points. We find that over the last decade there is some evidence that abnormal growth contributions are better indicators for troughs than for peaks.Business Cycle dating, HP filtering, exploratory turning point analysis, lead and lag indicators, abnormal growth contributions, gross domestic product (GDP) growth