Risk Attitude And Wage Growth: Replication And Reconstruction

We replicate Shaw (1996) who found that individual wage growth is higher for individuals with greater preference for risk taking. Expanding her dataset with more American observations and data for Germany, Spain and Italy, we find mixed support for the earlier results. We present and estimate a new model and find that in particular the wage level is sensitive to attitudes towards risk taking. Comments given at the Labour Economics Conference in honour of Niels Westergaard (Nyborg, August 2008) and EALE 2008 (Amsterdam) and at seminars in Maastricht,Reus and Essen (RWI) are gratefully acknowledged. The authors also acknowledge financial support from the Spanish Ministry of Science and Innovation (grant number SEJ2007-66318) and from the Barcelona Economics Program of CREA. JEL code: J24; J30. Key words: wage growth, risk, post-school investment.

Consumer populations and nutritional transition in Spain in the 20th century: a methodology for their reconstruction

One feature of the modern nutrition transition is the growing consumption of animal proteins. The most common approach in the quantitative analysis of this change used to be the study of averages of food consumption. But this kind of analysis seems to be incomplete without the knowledge of the number of consumers. Data about consumers are not usually published in historical statistics. This article introduces a methodological approach for reconstructing consumer populations. This methodology is based on some assumptions about the diffusion process of foodstuffs and the modeling of consumption patterns with a log-normal distribution. This estimating process is illustrated with the specific case of milk consumption in Spain between 1925 and 1981. These results fit quite well with other data and indirect sources available showing that this dietary change was a slow and late process. The reconstruction of consumer population could shed a new light in the study of nutritional transitions.

Reconstruction threshold for the hardcore model

"Vegeu el resum a l'inici del document del fitxer adjunt."

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

Reconstruction of a superscheme from its derived category

The aim of this paper is to construct a "super" version of a tensor triangulated category, and to show that super-schemes can be reconstructed from its category of perfect complexes in a way similar to Balmer [Bal05] provided we consider this extra structure.

The Sustainable Debts of Philip II: A Reconstruction of Spain's Fiscal Position, 1560-1598

The defaults of Philip II have attained mythical status as the origin of sovereign debt crises. Four times during his reign the king failed to honor his debts and had to renegotiate borrowing contracts. In this paper, we reassess the fiscal position of Habsburg Spain. New archival evidence allows us to derive comprehensive estimates of debt and revenue. These show that primary surpluses were sufficient to make the king's debt sustainable in most scenarios. Spain's debt burden was manageable up to the 1580s, and its fiscal position only deteriorated for good after the defeat of the "Invincible Armada." We also estimate fiscal policy reaction functions, and show that Spain under the Habsburgs was at least as "responsible" as the US in the 20th century or as Britain in the 18th century. Our results suggest that the outcome of uncertain events such as wars may influence on a history of default more than strict adherence to fiscal rules.

The sustainable debts of Philip II: A reconstruction of Castile's fiscal position, 1566-1596

The defaults of Philip II have attained mythical status as the origin of sovereigndebt crises. We reassess the fiscal position of Habsburg Castile, derivingcomprehensive estimates of revenue, debt, and expenditure from new archivaldata. The king s debts were sustainable. Primary surpluses were large and rising.Debt-to-revenue ratios remained broadly unchanged during Philip s reign.Castilian finances in the sixteenth century compare favorably with those of otherearly modern fiscal states at the height of their imperial ambitions, includingBritain. The defaults of Philip II therefore reflected short-term liquidity crises,and were not a sign of unsustainable debts.

Identification of geometrically consistent interest points for 3D scene reconstruction

We propose an algorithm that extracts image features that are consistent with the 3D structure of the scene. The features can be robustly tracked over multiple views and serve as vertices of planar patches that suitably represent scene surfaces, while reducing the redundancy in the description of 3D shapes. In other words, the extracted features will off er good tracking properties while providing the basis for 3D reconstruction with minimum model complexity

A fast Bayesian reconstruction algorithm for emission tomography with entropy prior converging to feasible images

The development and tests of an iterative reconstruction algorithm for emission tomography based on Bayesian statistical concepts are described. The algorithm uses the entropy of the generated image as a prior distribution, can be accelerated by the choice of an exponent, and converges uniformly to feasible images by the choice of one adjustable parameter. A feasible image has been defined as one that is consistent with the initial data (i.e. it is an image that, if truly a source of radiation in a patient, could have generated the initial data by the Poisson process that governs radioactive disintegration). The fundamental ideas of Bayesian reconstruction are discussed, along with the use of an entropy prior with an adjustable contrast parameter, the use of likelihood with data increment parameters as conditional probability, and the development of the new fast maximum a posteriori with entropy (FMAPE) Algorithm by the successive substitution method. It is shown that in the maximum likelihood estimator (MLE) and FMAPE algorithms, the only correct choice of initial image for the iterative procedure in the absence of a priori knowledge about the image configuration is a uniform field.

Bayesian image reconstruction with space-variant noise suppression

In this paper we present a Bayesian image reconstruction algorithm with entropy prior (FMAPE) that uses a space-variant hyperparameter. The spatial variation of the hyperparameter allows different degrees of resolution in areas of different statistical characteristics, thus avoiding the large residuals resulting from algorithms that use a constant hyperparameter. In the first implementation of the algorithm, we begin by segmenting a Maximum Likelihood Estimator (MLE) reconstruction. The segmentation method is based on using a wavelet decomposition and a self-organizing neural network. The result is a predetermined number of extended regions plus a small region for each star or bright object. To assign a different value of the hyperparameter to each extended region and star, we use either feasibility tests or cross-validation methods. Once the set of hyperparameters is obtained, we carried out the final Bayesian reconstruction, leading to a reconstruction with decreased bias and excellent visual characteristics. The method has been applied to data from the non-refurbished Hubble Space Telescope. The method can be also applied to ground-based images.

Super-resolution of remotely sensed images with variable-pixel linear reconstruction

This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher resolution image. We show that it can make significant spatial resolution improvements to satellite images of the Earth¿s surface allowing recognition of objects with size approaching the limiting spatial resolution of the lower resolution images. The algorithm is based on the Variable-Pixel Linear Reconstruction algorithm developed by Fruchter and Hook, a well-known method in astronomy but never used for Earth remote sensing purposes. The algorithm preserves photometry, can weight input images according to the statistical significance of each pixel, and removes the effect of geometric distortion on both image shape and photometry. In this paper, we describe its development for remote sensing purposes, show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones, and show applications to: 1) a set of simulated multispectral images obtained from a real Quickbird image; and 2) a set of multispectral real Landsat Enhanced Thematic Mapper Plus (ETM+) images. These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images, without amplifying the noise, and with very few artifacts.

Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals

A systematic assessment of global neural network connectivity through direct electrophysiological assays has remained technically infeasible, even in simpler systems like dissociated neuronal cultures. We introduce an improved algorithmic approach based on Transfer Entropy to reconstruct structural connectivity from network activity monitored through calcium imaging. We focus in this study on the inference of excitatory synaptic links. Based on information theory, our method requires no prior assumptions on the statistics of neuronal firing and neuronal connections. The performance of our algorithm is benchmarked on surrogate time series of calcium fluorescence generated by the simulated dynamics of a network with known ground-truth topology. We find that the functional network topology revealed by Transfer Entropy depends qualitatively on the time-dependent dynamic state of the network (bursting or non-bursting). Thus by conditioning with respect to the global mean activity, we improve the performance of our method. This allows us to focus the analysis to specific dynamical regimes of the network in which the inferred functional connectivity is shaped by monosynaptic excitatory connections, rather than by collective synchrony. Our method can discriminate between actual causal influences between neurons and spurious non-causal correlations due to light scattering artifacts, which inherently affect the quality of fluorescence imaging. Compared to other reconstruction strategies such as cross-correlation or Granger Causality methods, our method based on improved Transfer Entropy is remarkably more accurate. In particular, it provides a good estimation of the excitatory network clustering coefficient, allowing for discrimination between weakly and strongly clustered topologies. Finally, we demonstrate the applicability of our method to analyses of real recordings of in vitro disinhibited cortical cultures where we suggest that excitatory connections are characterized by an elevated level of clustering compared to a random graph (although not extreme) and can be markedly non-local.

Phytolith and spherulite studies of herbivores dung from the African Savannah as a tool for palaeocological reconstruction.

Los restos fecales están compuestos mayoritariamente por materia orgánica, la cual se degrada con el tiempo despareciendo finalmente del registro arqueológico. Sin embargo, estos restos fecales también contienen ciertos elementos resistentes al paso del tiempo y a los efectos postdeposicionales. Las esferulitas son cristales de carbonato cálcico formadas en los intestinos de ciertos animales herbívoros, principalmente rumiantes y que posteriormente son depositados en los restos fecales. Los fitolitos de sílice, aunque se forman en las plantas, son también comúnmente identificados en los restos fecales de animales herbívoros. Su número y morfología dependerá de la dieta vegetal de estos animales. El estudio que aquí se presenta se centra en el análisis microscópico de ambos elementos, fitolitos y esferulitas, identificados en restos fecales, de varios animales herbívoros, recolectados durante la estación seca en la Garganta de Olduvai en Tanzania. Los fitolitos y las esferulitas fueron identificados y analizados siguiendo un método morfológico y cuantitativo. Los fitolitos fueron luego comparados con una colección de referencia de plantas modernas de la misma zona geográfica con el propósito de estudiar la dieta de cada uno de los animales analizados. Finalmente los resultados fueron relacionados con los obtenidos del estudio de esferulitas, con el propósito de analizar la relación entre morfología y número de fitolitos y morfología y número de esferulitas para cada uno de los restos fecales analizados. El objetivo de este trabajo consiste en evaluar la utilidad de combinar ambas técnicas para identificar restos fecales en el registro arqueológico y, consecuentemente, responder a cuestiones relacionadas con el animal productor de estos restos, su dieta y movimientos migratorios y, paralelamente, la paleovegetación y el paleopaisaje en una región determinada.

Reconstruction of networks from their betweenness centrality

In this paper we study the reconstruction of a network topology from the values of its betweenness centrality, a measure of the influence of each of its nodes in the dissemination of information over the network. We consider a simple metaheuristic, simulated annealing, as the combinatorial optimization method to generate the network from the values of the betweenness centrality. We compare the performance of this technique when reconstructing different categories of networks –random, regular, small-world, scale-free and clustered–. We show that the method allows an exact reconstruction of small networks and leads to good topological approximations in the case of networks with larger orders. The method can be used to generate a quasi-optimal topology fora communication network from a list with the values of the maximum allowable traffic for each node.