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.

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.

Feature Point Detection and Curve Approximation for Early Processing of Freehand Sketches

Freehand sketching is both a natural and crucial part of design, yet is unsupported by current design automation software. We are working to combine the flexibility and ease of use of paper and pencil with the processing power of a computer to produce a design environment that feels as natural as paper, yet is considerably smarter. One of the most basic steps in accomplishing this is converting the original digitized pen strokes in the sketch into the intended geometric objects using feature point detection and approximation. We demonstrate how multiple sources of information can be combined for feature detection in strokes and apply this technique using two approaches to signal processing, one using simple average based thresholding and a second using scale space.

Predicting random level and seasonality of hotel prices. A structural equation growth curve approach

This article examines the effect on price of different characteristics of holiday hotels in the sun-and-beach segment, under the hedonic function perspective. Monthly prices of the majority of hotels in the Spanish continental Mediterranean coast are gathered from May to October 1999 from the tour operator catalogues. Hedonic functions are specified as random-effect models and parametrized as structural equation models with two latent variables, a random peak season price and a random width of seasonal fluctuations. Characteristics of the hotel and the region where they are located are used as predictors of both latent variables. Besides hotel category, region, distance to the beach, availability of parking place and room equipment have an effect on peak price and also on seasonality. 3- star hotels have the highest seasonality and hotels located in the southern regions the lowest, which could be explained by a warmer climate in autumn

The crime kuznets curve

We document the existence of a Crime Kuznets Curve in US states since the 1970s. As income levels have risen, crime has followed an inverted U-shaped pattern, first increasing and then dropping. The Crime Kuznets Curve is not explained by income inequality. In fact, we show that during the sample period inequality has risen monotonically with income, ruling out the traditional Kuznets Curve. Our finding is robust to adding a large set of controls that are used in the literature to explain the incidence of crime, as well as to controlling for state and year fixed effects. The Curve is also revealed in nonparametric specifications. The Crime Kuznets Curve exists for property crime and for some categories of violent crime.

Volcano.

Para aprender cómo y por qué se forman los volcanes y la manera en que contribuyen a la creación de nuevas rocas y tierra y la manera en que han afectado a la vida humana a través de milenios. Muestra dónde y por qué ocurren, la forma en que se pueden predecir y cómo afectan a la vida vegetal y animal. Qué le pasó a Pompeya y Herculano cuando el Vesubio entró en erupción, qué pasó cuando lo hizo el Krakatoa en Indonesia, así como qué hacer en un terremoto, cómo se forman los géiseres y lo que causa un maremoto.Tiene glosario y cronograma. Indicado a partir de nueve años.

Dating the volcanic eruption at Thera

The eruption of the volcano at Thera (Santorini) in the Aegean Sea undoubtedly had a profound influence on the civilizations of the surrounding region. The date of the eruption has been a subject of much controversy because it must be linked into the established and intricate archaeological phasings of both the prehistoric Aegean and the wider east Mediterranean. Radiocarbon dating of material from the volcanic destruction layer itself can provide some evidence for the date of the eruption, but because of the shape of the calibration curve for the relevant period, the value of such dates relies on there being no biases in the data sets. However, by dating the material from phases earlier and later than the eruption, some of the problems of the calibration data set can be circumvented and the chronology for the region can be resolved with more certainty. In this paper, we draw together the evidence we have accumulated so far, including new data on the destruction layer itself and for the preceding cultural horizon at Thera, and from associated layers at Miletos in western Turkey. Using Bayesian models to synthesize the data and to identify outliers, we conclude from the most reliable C-14 evidence (and using the INTCAL98 calibration data set) that the eruption of Thera occurred between 1663 and 1599 BC.

Lava production at Soufriere Hills Volcano, Montserrat: 1995-2009

[1] We estimate that about 1 km3 of andesitic lava has been produced at Soufrière Hills Volcano, Montserrat from 1995 to 2009. There were three major episodes of extrusion, each lasting about 2 to 3.5 years and producing about 280 to 340 M m3 of lava, and one minor episode. Our estimates account for the dense rock equivalent volumetric contributions from the core and talus components of the lava dome, pyroclastic flow deposits and air-fall deposits. By 2005 at least two thirds of the erupted mass has already entered the sea. The average lava flux across the major extrusion episodes has been 3–5 m3s−1, with short-period (10–15 days) pulses up to 10–20 m3s−1. The first and third episodes of extrusion show similar flux histories suggesting similar behaviour of the system ten years apart. Waning flux towards the end of each episode may be caused by declining overpressure in the magma reservoir.

Ground deformation at Soufriere Hills Volcano, Montserrat during 1998-2000 measured by radar interferometry and GPS

We examine the motion of the ground surface on the Soufriere Hills Volcano, Montserrat between 1998 and 2000 using radar interferometry (InSAR). To minimise the effects of variable atmospheric water vapour on the InSAR measurements we use independently-derived measurements of the radar path delay from six continuous GPS receivers. The surfaces providing a measurable inter-ferometric signal are those on pyroclastic flow deposits, mainly emplaced in 1997. Three types of surface motion can be discriminated. Firstly, the surfaces of thick, valley-filling deposits subsided at rates of 150-120 mm/year in the year after emplacement to 50-30 mm/year two years later. This must be due to contraction and settling effects during cooling. The second type is the near-field motion localised within about one kilometre of the dome. Both subsidence and uplift events are seen and though the former could be due to surface gravitational effects, the latter may reflect shallow (< 1 km) pressurisation effects within the conduit/dome. Far-field motions of the surface away from the deeply buried valleys are interpreted as crustal strains. Because the flux of magma to the surface stopped from March 1998 to November 1999 and then resumed from November 1999 through 2000, we use InSAR data from these two periods to test the crustal strain behaviour of three models of magma supply: open, depleting and unbalanced. The InSAR observations of strain gradients of 75-80 mm/year/krn uplift during the period of quiescence on the western side of the volcano are consistent with an unbalanced model in which magma supply into a crustal magma chamber continues during quiescence, raising chamber pressure that is then released upon resumption of effusion. GPS motion vectors agree qualitatively with the InSAR displacements but are of smaller magnitude. The discrepancy may be due to inaccurate compensation for atmospheric delays in the InSAR data. (c) 2005 Elsevier B.V. All rights reserved.

Insights into processes and deposits of hazardous vulcanian explosions at Soufrière Hills Volcano during 2008 and 2009 (Montserrat, West Indies)

During the Soufrière Hills eruption, vulcanian explosions have generally occurred 1) in episodic cycles; 2) isolated during pauses in extrusion, and 3) after major collapses of the dome. In a different eruptive context, significant vulcanian explosions occurred on 29 July 2008, 3 December 2008, and 3 January 2009. Deposits are pumiceous except for the 3 December event. We reconstructed the dispersal pattern of the deposits and their textural characteristics to evaluate erupted volume and vesicularity of the magma at fragmentation. We discuss the implications of these explosions in terms of eruptive processes and chronology, and the hazards posed by their sudden and often unheralded occurrence. We suggest that overpressurization of the conduit can develop over time-scales of months to weeks by a process of self-sealing of conduit walls and/or the cooling dome by silica polymorphs. This work provides new insights for understanding the generation of hazardous vulcanian explosions at andesitic volcanoes.