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

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

Quasinormal modes in kink excitations and kink-antikink interactions: a toy model

We study excitations and collisions of kinks in a scalar field theory where the potential has two minima with $Z_2$ symmetry. The field potential is designed to create a square well potential in the stability equation of the kink excitations. The stability equation is analogous to the Schr\"{o}dinger equation, and therefore we use quantum mechanics techniques to study the system. We modify the square well potential continuously, which allows the excitation to tunnel and consequently turns the normal modes of the kink into quasinormal modes. We study the effect of this transition, leading to energy leak, on isolated kink excitations. Finally, we investigate kink-antikink collisions and the resulting scaling and fractal structure of the resonance windows considering both normal and quasinormal modes and compare the results.Comment: 23 pages, 11 figure

Capacity and tendency: A neuroscientific framework for the study of emotion regulation.

It is widely accepted that the ability to effectively regulate one's emotions is a cornerstone of physical and mental health. As such, it should come as no surprise that the number of neuroimaging studies focused on emotion regulation and associated processes has increased exponentially in the past decade. To date, neuroimaging research on this topic has examined two distinct but complementary features of emotion regulation - the capacity to effectively utilize a strategy to regulate emotion and to a lesser extent, the tendency to choose to regulate. However, theoretical accounts of emotion regulation have only recently begun to distinguish capacity from tendency. In the present review, we provide a novel framework for conceptualizing these two intertwined, yet distinct, facets of emotion regulation. First we characterize brain regions that support emotion generation and are thus targeted by emotion regulation. Next, we synthesize findings from the dozens of neuroimaging studies that have examined emotion regulation capacity, focusing in particular on the most commonly studied emotion regulation strategy - reappraisal. Finally, we discuss emerging neuroimaging research examining state and trait regulatory tendencies. We conclude by integrating findings from neuroimaging research on emotion regulation capacity and tendency and suggest ways that this integrated model can inform basic and translational neuroscientific research on emotion regulation