Multi-feature Bottom-up Processing and Top-down Selection for an Object-based Visual Attention Model

Artificial vision systems can not process all the information that they receive from the world in real time because it is highly expensive and inefficient in terms of computational cost. However, inspired by biological perception systems, it is possible to develop an artificial attention model able to select only the relevant part of the scene, as human vision does. This paper presents an attention model which draws attention over perceptual units of visual information, called proto-objects, and which uses a linear combination of multiple low-level features (such as colour, symmetry or shape) in order to calculate the saliency of each of them. But not only bottom-up processing is addressed, the proposed model also deals with the top-down component of attention. It is shown how a high-level task can modulate the global saliency computation, modifying the weights involved in the basic features linear combination.Ministerio de Economía y Competitividad (MINECO), proyectos: TIN2008-06196 y TIN2012-38079-C03-03. Campus de Excelencia Internacional Andalucía Tech

Arquitectura en tiempo real. Algunas formas de hacer.

La intervención de Jesús Estepa Rubio, como representante del equipo ER arquitectos, se centró en la idea de transmitir motivación y optimismo ante una situación que, aunque pueda resultar dura para la profesión de arquitecto, puede suponer ventajas para el resultado del ejercicio arquitectónico. El deseo de querer hacer un buen trabajo, unido al estudio y el entendimiento de las condiciones de contorno que rodean el hecho arquitectónico resultan fundamentales para conseguir buenos resultados.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Estimating Macroeconomic Models: A Likelihood Approach

This paper shows how particle filtering allows us to undertake likelihood-based inference in dynamic macroeconomic models. The models can be nonlinear and/or non-normal. We describe how to use the output from the particle filter to estimate the structural parameters of the model, those characterizing preferences and technology, and to compare different economies. Both tasks can be implemented from either a classical or a Bayesian perspective. We illustrate the technique by estimating a business cycle model with investment-specific technological change, preference shocks, and stochastic volatility.

Estimating Nonlinear Dynamic Equilibrium economies: A Likelihood Approach

This paper presents a framework to undertake likelihood-based inference in nonlinear dynamic equilibrium economies. We develop a Sequential Monte Carlo algorithm that delivers an estimate of the likelihood function of the model using simulation methods. This likelihood can be used for parameter estimation and for model comparison. The algorithm can deal both with nonlinearities of the economy and with the presence of non-normal shocks. We show consistency of the estimate and its good performance in finite simulations. This new algorithm is important because the existing empirical literature that wanted to follow a likelihood approach was limited to the estimation of linear models with Gaussian innovations. We apply our procedure to estimate the structural parameters of the neoclassical growth model.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Sequential Monte Carlo)

Convergence Properties of the Likelihood of Computed Dynamic Models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed-form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods? First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods.

Estimating Dynamic Equilibrium Economies: Linear versus Nonlinear Likelihood

This paper compares two methods for undertaking likelihood-based inference in dynamic equilibrium economies: a Sequential Monte Carlo filter proposed by Fernández-Villaverde and Rubio-Ramírez (2004) and the Kalman filter. The Sequential Monte Carlo filter exploits the nonlinear structure of the economy and evaluates the likelihood function of the model by simulation methods. The Kalman filter estimates a linearization of the economy around the steady state. We report two main results. First, both for simulated and for real data, the Sequential Monte Carlo filter delivers a substantially better fit of the model to the data as measured by the marginal likelihood. This is true even for a nearly linear case. Second, the differences in terms of point estimates, even if relatively small in absolute values, have important effects on the moments of the model. We conclude that the nonlinear filter is a superior procedure for taking models to the data.Likelihood-Based Inference, Dynamic Equilibrium Economies, Nonlinear Filtering, Kalman Filter, Sequential Monte Carlo

Comparing dynamic equilibrium economies to data

This paper studies the properties of the Bayesian approach to estimation and comparison of dynamic equilibrium economies. Both tasks can be performed even if the models are nonnested, misspecified, and nonlinear. First, the authors show that Bayesian methods have a classical interpretation: asymptotically the parameter point estimates converge to their pseudotrue values, and the best model under the Kullback-Leibler will have the highest posterior probability. Second, they illustrate the strong small sample behavior of the approach using a well-known application: the U.S. cattle cycle. Bayesian estimates outperform maximum likelihood results, and the proposed model is easily compared with a set of BVARs.Econometric models

Bayesian multiparameter quantum metrology with limited data

A longstanding problem in quantum metrology is how to extract as much information as possible in realistic scenarios with not only multiple unknown parameters, but also limited measurement data and some degree of prior information. Here we present a practical solution to this: We derive a Bayesian multi-parameter quantum bound, construct the optimal measurement when our bound can be saturated for a single shot, and consider experiments involving a repeated sequence of these measurements. Our method properly accounts for the number of measurements and the degree of prior information, and we illustrate our ideas with a qubit sensing network and a model for phase imaging, clarifying the nonasymptotic role of local and global schemes. Crucially, our technique is a powerful way of implementing quantum protocols in a wide range of practical scenarios that tools such as the Helstrom and Holevo Cramér-Rao bounds cannot normally access

Special Issue “Cognitive Robotics”

Within the realm of new robotics, researchers have placed a great amount of effort into learning, understanding, and representing knowledge for task execution by robots. The goal is to develop robots that can help humans with daily tasks. Cognitive robots need to explore and understand their environment, choose a safe and human-aware course of action, and learn—not only from experience, but also through interaction. This Special Issue collects nine research papers in various fields related to Cognitive robotics. The relevance of the knowledge representation and its use by decision makers is present in the proposal by Martín et al. [1]. Specifically, the necessity of integrating behaviors and symbolic knowledge was solved by adding a graph-based working memory to a cognitive robotics architecture. The proposed framework has been successfully tested in robotics competitions such as the RoboCup and the European Robotics League. The aim of combining deliberative and reactive behaviors in a flexible way is also present in the work by González-Santamarta et al. [2]. In the MERLIN cognitive architecture, the process of integrating deliberative and behavioral-based mechanisms in robotics is normalized. The solution is empirically tested using a variation of the challenge defined in the SciRoc @ home competition. The relevance that cognitive robots can provide for improving task effectiveness and productivity in the industrial domain is highlighted in the work by Chacón et al. [3]. 8. (...)This work has been partially funded by the RTI2018-099522-B-C41 project, funded by the Spanish Ministerio de Ciencia, Innovación y Universidades and FEDER funds

MEDEA: A DSGE Model for the Spanish Economy

In this paper, we provide a brief introduction to a new macroeconometric model of the Spanish economy named MEDEA (Modelo de Equilibrio Dinámico de la Economía EspañolA). MEDEA is a dynamic stochastic general equilibrium (DSGE) model that aims to describe the main features of the Spanish economy for policy analysis, counterfactual exercises, and forecasting. MEDEA is built in the tradition of New Keynesian models with real and nominal rigidities, but it also incorporates aspects such as a small open economy framework, an outside monetary authority such as the ECB, and population growth, factors that are important in accounting for aggregate fluctuations in Spain. The model is estimated with Bayesian techniques and data from the last two decades. Beyond describing the properties of the model, we perform different exercises to illustrate the potential of MEDEA, including historical decompositions, long-run and short-run simulations, and counterfactual experiments.DSGE Models, Likelihood Estimation, Bayesian Methods