Investment Cost Channel and Monetary Transmission

We show that a standard DSGE model with investment cost channels has important model stability and policy implications. Our analysis suggests that in economies characterized by supply side well as demand side channels of monetary transmission, policymakers may have to resort to a much more aggressive stand against inflation to obtain locally unique equilibrium. In such an environment targeting output gap may cause model instability. We also show that it is difficult to distinguish between the New Keynesian model and labor cost channel only case, while with investment cost channel differences are more significant. This result is important as it suggests that if one does not take into account the investment cost channel, one is underestimating the importance of supply side effects.Cost channel, Investment finance, Taylor Rule, indeterminacy

Automatic detection of geospatial objects using multiple hierarchical segmentations

Cataloged from PDF version of article.The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classi- fication. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback–Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes

Unsupervised detection and localization of structural textures using projection profiles

Cataloged from PDF version of article.The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.(C)2010 Elsevier Ltd. All rights reserved

Foreword to the special issue on pattern recognition in remote sensing

Cataloged from PDF version of article.The nine papers in this special issue focus on covering different aspects of remote sensing image analysis. © 2012 IEE

Maximum likelihood estimation of Gaussian mixture models using stochastic search

Cataloged from PDF version of article.Gaussian mixture models (GMM), commonly used in pattern recognition and machine learning, provide a flexible probabilistic model for the data. The conventional expectation-maximization (EM) algorithm for the maximum likelihood estimation of the parameters of GMMs is very sensitive to initialization and easily gets trapped in local maxima. Stochastic search algorithms have been popular alternatives for global optimization but their uses for GMM estimation have been limited to constrained models using identity or diagonal covariance matrices. Our major contributions in this paper are twofold. First, we present a novel parametrization for arbitrary covariance matrices that allow independent updating of individual parameters while retaining validity of the resultant matrices. Second, we propose an effective parameter matching technique to mitigate the issues related with the existence of multiple candidate solutions that are equivalent under permutations of the GMM components. Experiments on synthetic and real data sets show that the proposed framework has a robust performance and achieves significantly higher likelihood values than the EM algorithm. (C) 2012 Elsevier Ltd. All rights reserved

Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery

Cataloged from PDF version of article.Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics

Automatic detection and segmentation of orchards using very high-resolution imagery

Cataloged from PDF version of article.Spectral information alone is often not sufficient to distinguish certain terrain classes such as permanent crops like orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive spatial structures that can be observable in detail in very high spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards. The detection step uses a texture model that is based on the idea that textures are made up of primitives (trees) appearing in a near-regular repetitive arrangement (planting patterns). The algorithm starts with the enhancement of potential tree locations by using multi-granularity isotropic filters. Then, the regularity of the planting patterns is quantified using projection profiles of the filter responses at multiple orientations. The result is a regularity score at each pixel for each granularity and orientation. Finally, the segmentation step iteratively merges neighboring pixels and regions belonging to similar planting patterns according to the similarities of their regularity scores and obtains the boundaries of individual orchards along with estimates of their granularities and orientations. Extensive experiments using Ikonos and QuickBird imagery as well as images taken from Google Earth show that the proposed algorithm provides good localization of the target objects even when no sharp boundaries exist in the image data. © 2012 IEEE

Pathological yawning in patients with acute middle cerebral artery infarction: Prognostic significance and association with the infarct location

Background: Pathological yawning is a compulsive, frequent, repetitive yawning triggered by a specific reason besides fatigue or boredom. It may be related to iatrogenic, neurologic, psychiatric, gastrointestinal, or metabolic disorders. Moreover, it could also be seen in the course of an ischemic stroke. Aims: To determine whether pathological yawning is a prognostic marker of middle cerebral artery stroke and evaluate its relationship with the infarct location. Study Design: Cross-sectional study. Methods: We examined 161 patients with acute middle cerebral artery stroke, consecutively admitted to emergency department. Demographic information, stroke risk factors, stroke type according to Trial of Org 10172 in Acute Stroke Treatment classification, blood oxygen saturation, body temperature, blood pressure, heart rate, glucose levels, daytime of stroke onset, National Institutes of Health Stroke Scale score (National Institutes of Health Stroke Scale score, at admission and 24 h), modified Rankin scale (at 3 months), and infarct locations were documented. Pathological yawning was defined as ?3 yawns/15 min. All patients were observed for 6 hours to detect pathological yawning. National Institutes of Health Stroke Scale score >10 was determined as severe stroke. The correlation between the presence of pathological yawning and stroke severity, infarct location, and the short-and long-term outcomes of the patients were evaluated. Results: Sixty-nine (42.9%) patients had pathological yawning and 112 (69.6%) had cortical infarcts. Insular and opercular infarcts were detected in 65 (40.4%) and 54 (33.5%) patients, respectively. Pathological yawning was more frequently observed in patients with cortical, insular, and opercular infarcts (p<0.05). Pathological yawning was related to higher National Institutes of Health Stroke Scale scores. Patients with severe stroke (National Institutes of Health Stroke Scale score ?10) presented with more pathological yawning than those with mild to moderate strokes (p<0.05). The clinical outcomes and mortality rates showed no significant relationship with the occurrence of pathological yawning. Conclusion: Pathological yawning in middle cerebral artery stroke was associated with stroke severity, presence of cortical involvement, and insular and opercular infarcts. However, no association was found with long-term outcome and mortality. ©Copyright 2020 by Trakya University Faculty of Medicine / The Balkan Medical Journal published by Galenos Publishing House