Inflation dynamics and the New Keynesian Phillips Curve: an identification robust econometric analysis

In this paper, we use identification-robust methods to assess the empirical adequacy of a New Keynesian Phillips Curve (NKPC) equation. We focus on the Gali and Gertler’s (1999) specification, on both U.S. and Canadian data. Two variants of the model are studied: one based on a rationalexpectations assumption, and a modification to the latter which consists in using survey data on inflation expectations. The results based on these two specifications exhibit sharp differences concerning: (i) identification difficulties, (ii) backward-looking behavior, and (ii) the frequency of price adjustments. Overall, we find that there is some support for the hybrid NKPC for the U.S., whereas the model is not suited to Canada. Our findings underscore the need for employing identificationrobust inference methods in the estimation of expectations-based dynamic macroeconomic relations.

Of master Hongyi's last calligraphic work

Hongyi (1860-1942) est le maître de l’école bouddhiste appelée « Terre Pure » en Chine moderne. Dans l'histoire du bouddhisme chinois, il est le seul maître bouddhiste qui peut se classer parmi les plus grands calligraphes chinois. Dans l'histoire de la calligraphie chinoise, il est le premier moine-calligraphe qui allie sa foi bouddhiste avec son art calligraphique tout en appliquant des principes de la peinture occidentale à son travail de calligraphie. Son art serve bien sa croyance religieuse et n’est pas indépendent de celle-ci, ce qui est extraordinaire dans l’histoire calligraphique. Il utilise le concept bouddhiste de «une forme sans forme» pour interpréter la calligraphie chinoise, et propose une série de théories calligraphiques bouddhistes pour guider sa pratique. D’ailleurs, sa calligraphie ne sert qu'à retranscrire les textes bouddhistes classiques et contemporains. Ses œuvres illustrent donc l'influence du bouddhisme chinois sur la calligraphie chinoise. Traditionnellement, la dernière œuvre d'un calligraphe est souvent considérée comme son chef-d'œuvre en fonction de deux critères: sa technique et son contenu moral. En outre, quelques-unes des dernières œuvres de grands calligraphes ont survécu. La rareté les rend encore plus précieuses. Bei xin ji jiao («悲欣交集») est la dernière œuvre de Hongyi, qui a été achevée trois jours avant son nirvana. Ce mémoire sera d'étudier et d'analyser l'influence de sa pensée bouddhiste sur le contenu littéraire et la forme artistique de cette œuvre, et d’expliciter qu’elle guide pleinement vers l'état ultime de sa pratique bouddhiste au plus haut niveau de sa calligraphie.

Scoliosis curve type classification from 3D trunk image

Adolescent idiopathic scoliosis (AIS) is a deformity of the spine manifested by asymmetry and deformities of the external surface of the trunk. Classification of scoliosis deformities according to curve type is used to plan management of scoliosis patients. Currently, scoliosis curve type is determined based on X-ray exam. However, cumulative exposure to X-rays radiation significantly increases the risk for certain cancer. In this paper, we propose a robust system that can classify the scoliosis curve type from non invasive acquisition of 3D trunk surface of the patients. The 3D image of the trunk is divided into patches and local geometric descriptors characterizing the surface of the back are computed from each patch and forming the features. We perform the reduction of the dimensionality by using Principal Component Analysis and 53 components were retained. In this work a multi-class classifier is built with Least-squares support vector machine (LS-SVM) which is a kernel classifier. For this study, a new kernel was designed in order to achieve a robust classifier in comparison with polynomial and Gaussian kernel. The proposed system was validated using data of 103 patients with different scoliosis curve types diagnosed and classified by an orthopedic surgeon from the X-ray images. The average rate of successful classification was 93.3% with a better rate of prediction for the major thoracic and lumbar/thoracolumbar types.

Non invasive classification system of scoliosis curve types using least-squares support vector machines

Objective To determine scoliosis curve types using non invasive surface acquisition, without prior knowledge from X-ray data. Methods Classification of scoliosis deformities according to curve type is used in the clinical management of scoliotic patients. In this work, we propose a robust system that can determine the scoliosis curve type from non invasive acquisition of the 3D back surface of the patients. The 3D image of the surface of the trunk is divided into patches and local geometric descriptors characterizing the back surface are computed from each patch and constitute the features. We reduce the dimensionality by using principal component analysis and retain 53 components using an overlap criterion combined with the total variance in the observed variables. In this work, a multi-class classifier is built with least-squares support vector machines (LS-SVM). The original LS-SVM formulation was modified by weighting the positive and negative samples differently and a new kernel was designed in order to achieve a robust classifier. The proposed system is validated using data from 165 patients with different scoliosis curve types. The results of our non invasive classification were compared with those obtained by an expert using X-ray images. Results The average rate of successful classification was computed using a leave-one-out cross-validation procedure. The overall accuracy of the system was 95%. As for the correct classification rates per class, we obtained 96%, 84% and 97% for the thoracic, double major and lumbar/thoracolumbar curve types, respectively. Conclusion This study shows that it is possible to find a relationship between the internal deformity and the back surface deformity in scoliosis with machine learning methods. The proposed system uses non invasive surface acquisition, which is safe for the patient as it involves no radiation. Also, the design of a specific kernel improved classification performance.

Prediction of scoliosis curve type based on the analysis of trunk surface topography

Scoliosis treatment strategy is generally chosen according to the severity and type of the spinal curve. Currently, the curve type is determined from X-rays whose acquisition can be harmful for the patient. We propose in this paper a system that can predict the scoliosis curve type based on the analysis of the surface of the trunk. The latter is acquired and reconstructed in 3D using a non invasive multi-head digitizing system. The deformity is described by the back surface rotation, measured on several cross-sections of the trunk. A classifier composed of three support vector machines was trained and tested using the data of 97 patients with scoliosis. A prediction rate of 72.2% was obtained, showing that the use of the trunk surface for a high-level scoliosis classification is feasible and promising.