National industry cluster templates and the structure of industry output dynamics: a stochastic geometry approach

Cluster analysis has been widely used in an Input-Output framework, with the main objective of uncover the structure of production, in order to better identify which sectors are strongly connected with each other and choose the key sectors of a national or regional economy. There are many empirical studies determining potential clusters from interindustry flows directly, or from their corresponding technical (demand) or market (supply) coefficients, most of them applying multivariate statistical techniques. In this paper, after identifying clusters this way, and since it may be expected that strongly (interindustry) connected sectors share a similar growth and development path, the structure of sectoral dynamics is uncovered, by means of a stochastic geometry technique based on the correlations of industry outputs in a given period of time. An application is made, using Portuguese input-output data, and the results do not clearly support this expectation.Clusters, Input-output analysis, Industry output dynamics

New non-Gaussian feature in COBE-DMR Four Year Maps

We extend a previous bispectrum analysis of the Cosmic Microwave Background temperature anisotropy, allowing for the presence of correlations between different angular scales. We find a strong non-Gaussian signal in the ``inter-scale'' components of the bispectrum: their observed values concentrate close to zero instead of displaying the scatter expected from Gaussian maps. This signal is present over the range of multipoles $\ell=6 -18$, in contrast with previous detections. We attempt to attribute this effect to galactic foreground contamination, pixelization effects, possible anomalies in the noise, documented systematic errors studied by the COBE team, and the effect of assumptions used in our Monte Carlo simulations. Within this class of systematic errors the confidence level for rejecting Gaussianity varies between 97% and 99.8%.Comment: Replaced with revised version. Two typos in and around equation (3) have been corrected (components of bispectrum are of the form (l-1, l, l+1) with l even). Published in Ap.J.Let

Economic Impacts of Ageing: An Interindustry Approach

Purpose - The purpose of this paper is to quantify the impact of the evolution of consumption patterns associated with ageing on the relative importance of industries in Portugal. Design/Methodology/Approach - This paper uses data from the Family Spending Survey to disaggregate the Household column of the Portuguese Input-Output Table in different age groups, projecting their consumption, using the latest demographic projections made by Statistics Portugal (INE). Findings - The study identifies the industries that are likely to be stimulated by the ageing of the Portuguese populations, as well as the industries that will most likely become disadvantaged by the process. Social implications - The task of identification of growing and declining industries due to ageing is important to help the design of employment, environmental, and social policies. Original/Value - The contemporary demographic trends in western societies have added to the importance of studying the economic and social consequences of ageing. Previously, the main issues have been the labour market effects, the sustainability of social security systems, and long-term care. In this paper, we address a different research topic, quantifying the sectoral impact of the evolution of consumption patterns associated with ageing.Ageing; Input-output; Consumption behaviour.

D-ADMM: A Communication-Efficient Distributed Algorithm For Separable Optimization

We propose a distributed algorithm, named Distributed Alternating Direction Method of Multipliers (D-ADMM), for solving separable optimization problems in networks of interconnected nodes or agents. In a separable optimization problem there is a private cost function and a private constraint set at each node. The goal is to minimize the sum of all the cost functions, constraining the solution to be in the intersection of all the constraint sets. D-ADMM is proven to converge when the network is bipartite or when all the functions are strongly convex, although in practice, convergence is observed even when these conditions are not met. We use D-ADMM to solve the following problems from signal processing and control: average consensus, compressed sensing, and support vector machines. Our simulations show that D-ADMM requires less communications than state-of-the-art algorithms to achieve a given accuracy level. Algorithms with low communication requirements are important, for example, in sensor networks, where sensors are typically battery-operated and communicating is the most energy consuming operation.Comment: To appear in IEEE Transactions on Signal Processin

Distributed Optimization With Local Domains: Applications in MPC and Network Flows

In this paper we consider a network with $P$ nodes, where each node has exclusive access to a local cost function. Our contribution is a communication-efficient distributed algorithm that finds a vector $x^\star$ minimizing the sum of all the functions. We make the additional assumption that the functions have intersecting local domains, i.e., each function depends only on some components of the variable. Consequently, each node is interested in knowing only some components of $x^\star$, not the entire vector. This allows for improvement in communication-efficiency. We apply our algorithm to model predictive control (MPC) and to network flow problems and show, through experiments on large networks, that our proposed algorithm requires less communications to converge than prior algorithms.Comment: Submitted to IEEE Trans. Aut. Contro

Distributed Basis Pursuit

We propose a distributed algorithm for solving the optimization problem Basis Pursuit (BP). BP finds the least L1-norm solution of the underdetermined linear system Ax = b and is used, for example, in compressed sensing for reconstruction. Our algorithm solves BP on a distributed platform such as a sensor network, and is designed to minimize the communication between nodes. The algorithm only requires the network to be connected, has no notion of a central processing node, and no node has access to the entire matrix A at any time. We consider two scenarios in which either the columns or the rows of A are distributed among the compute nodes. Our algorithm, named D-ADMM, is a decentralized implementation of the alternating direction method of multipliers. We show through numerical simulation that our algorithm requires considerably less communications between the nodes than the state-of-the-art algorithms.Comment: Preprint of the journal version of the paper; IEEE Transactions on Signal Processing, Vol. 60, Issue 4, April, 201