Convergence Approaches Research Articles

NORMALIZING ACTIVE EMPLOYMENT POLICIES IN THE EUROPEAN UNION

ABSTRACT The paper examines some of the ways in which the problematic of activation is put into play in the European Employment Strategy (EES) and Danish employment policies. The limitations of causal analysis and convergence approaches are discussed and a different analytical framework based on the concept of normalization is proposed. It is argued that while various forms of activation were present in Denmark well before the launching of the EES in 1997/1998, the latter has contributed to the normalization of the Danish employment policies by sustaining activation as the key element of such policies and by disregarding political interventions based on alternative problematizations of labour and well-being.

Gaussian Mean-Shift Is an EM Algorithm

The mean-shift algorithm, based on ideas proposed by Fukunaga and Hostetler [16], is a hill-climbing algorithm on the density defined by a finite mixture or a kernel density estimate. Mean-shift can be used as a nonparametric clustering method and has attracted recent attention in computer vision applications such as image segmentation or tracking. We show that, when the kernel is Gaussian, mean-shift is an expectation-maximization (EM) algorithm and, when the kernel is non-Gaussian, mean-shift is a generalized EM algorithm. This implies that mean-shift converges from almost any starting point and that, in general, its convergence is of linear order. For Gaussian mean-shift, we show: 1) the rate of linear convergence approaches 0 (superlinear convergence) for very narrow or very wide kernels, but is often close to 1 (thus, extremely slow) for intermediate widths and exactly 1 (sublinear convergence) for widths at which modes merge, 2) the iterates approach the mode along the local principal component of the data points from the inside of the convex hull of the data points, and 3) the convergence domains are nonconvex and can be disconnected and show fractal behavior. We suggest ways of accelerating mean-shift based on the EM interpretation.

Convergence in the Americas: Some Lessons from the DR‐CAFTA Process

The proliferation of trade agreements in the Americas is a major cause for concern as it might fragment or inhibit the emergence of a hemispheric trading system. In addition to establishing a free trade area between the US and six countries in Latin America, the DR‐CAFTA consolidates a set of trade arrangements among those six Latin American countries. The approaches and techniques employed to achieve this consolidation should be studied by policy analysts and negotiators of broader hemispheric‐wide negotiations since the DR‐CAFTA is in fact a microcosm of the spaghetti bowl of trade agreements now existing in the Americas. This paper points out the potential consequences of the proliferation of trade agreements in the Americas and provides an analysis of the convergence techniques and approaches used in the DR‐CAFTA, in particular in the areas of market access and rules of origin.

Integrated community development/conflict resolution strategies as ‘peace building potential’ in South Africa and Northern Ireland

This paper argues that integrated community development and conflict resolution strategies could help to sustain peace in post-settlement contexts. The conceptual background to this argument is unfolded through the tracking of key concepts such as convergence, post-settlement contexts, civil society, community development, conflict resolution, peace building, and multi-track approaches to peace building. Empirical evidence for convergences at the practice level is presented through excerpts from two case studies, one from Northern Ireland and the other from South Africa. Some of the key issues around which community development/conflict resolution strategies coalesce relate to addressing human needs, encouraging people participation, and building democracy. These are critical peace building tasks. Despite the positive potential of such integrated strategies, both South Africa and Northern Ireland face crucial challenges.

How the Maastricht Criteria and the Stability and Growth Pact Affected the Convergence Process in the European Union: A Panel Data Analysis

The ECB monetary policy has been criticised for giving more attention to price stabilisation in Europe rather than encouraging higher growth and employment. It is now largely accepted that the Maastricht norms and the Stability Pact have imposed serious macroeconomic constraints on economic performance without taking into account specific problems of the individual economies. An interesting issue to examine is how the Maastricht obligations and the Stability Pact restrictions have affected the process of real convergence between the European economies in the last two decades. The purpose of this paper is to test for convergence in living standards, productivity, investment and unemployment among the European countries. From this analysis and using panel data estimation techniques, it is possible to detect any significant influence (favourable or not) of the Maastricht rules on real convergence. The proposed paper has the following structure: The introduction focuses on the importance of the Maastricht criteria and the Stability Pact. Part 2 explains the theoretical background of the convergence approaches, which are mainly used in the growth literature. Some stylised facts on the evolution of income per capita, output per worker, investment share and the performance of employment and unemployment rates are given in part 3, trying to identify any catch-up tendencies. The empirical evidence on real convergence, which is the core of the study, is given in section 4. The final section summarises and derives the main conclusions.

How do organizations implement downsizing? – An Australian and New Zealand study

The Australian and New Zealand banking industries have been cutting their workforces steadily since the mid-1990s. With further rounds of workforce downsizing predicted, it was of considerable interest and importance to examine the implementation strategies that large Australian and New Zealand banks have adopted in their latest downsizing endeavors. This study has revealed three major findings. First, Australian banks tended to primarily adopt workforce reduction strategies, whereas New Zealand banks employed a mixture of organization redesign strategies, workforce reduction strategies, and systemic strategies. Second, Australian banks were perceived to have considerable depth in their downsizing, whereas New Zealand banks had more breadth in their overall strategies. Third, Australian banks favored to adopt reorientation approaches, whereas New Zealand banks were more inclined to embrace reinforcement (convergence) approaches. It remains unclear as to why large Australian and New Zealand banks have diverged in their approaches and strategies to downsizing and in their differing selection of available implementation strategies. Government interference, executive remuneration, industrial relations demands, competitive national and international market pressures, and the downsizing history of individual industries and organizations, on the one hand, and differences in national cultures and cultural values on the other, may have influenced the adoption of downsizing implementation strategies. At the same time, it has also been shown that downsizing has engendered negative financial, organizational, and social consequences in both Australia and New Zealand.

Final comments by the authors

This paper investigates regional inequality from two standpoints. First, it explores them from a global perspective by assessing the incidence of economic growth for 2867 regions from 161 countries. Results show that middle income regions had the highest growth rates after the Great Recession, whilst regions from deciles 9 and, to a lower extent, those from decile 10 had suffered the most negative impact. Hence, the incidence of regional growth resembles the “elephant curve” of global inter-personal inequality. Second, the paper explores within-country regional inequality for a panel of 25 countries, over the period 2000–2018. Using OECD data and following both cross-sectional and time series approaches, results show that, while regional inequality decreases with growing GDPPC, regional polarization is more persistent and does not necessarily follow the same rule. The paper also delivers a first systematic assessment of sigma-convergence and regional polarization for a considerable number of countries, with findings pinpointing the importance of spatial clustering (which is contributing considerably to regional inequality levels), but also its complementarity with sigma-convergence measures (as regional inequality trajectories are not necessary associated with spatial clustering trajectories). Overall, findings also suggest that convergence approaches cannot provide an adequate theoretical background for understanding regional problems as long as they remain aspatial.

Reducing power consumption of turbo-code decoder using adaptive iteration with variable supply voltage

Turbo-code becomes popular for the next generation wireless communication systems because of its remarkable coding performance. One of the problems for decoding turbo-code in the receiver is the complexity and the high power consumption since multiple iterations of Soft Output Viterbi Algorithm (SOVA) or Maximum a posteriori (MAP) decoding have to be carried out to decode a data frame. To reduce the complexity of the turbo-code decoder, adaptive iteration based on cyclic redundancy checking (CRC) and output convergence approaches has been proposed to reduce the average number of iterations required for decoding a data frame. This results in a system that has variable workload since the amount of computation required for decoding each data frame is different. In this work, we propose a dynamic voltage scaling approach to further reduce the power consumption. Different from other variable workload systems, the workload here is not known at the time when the data is being decoded. Thus, optimum voltage assignment is not feasible. We propose several heuristic algorithms to assign supply voltage for different decoding iterations. Simulation results show that significant reduction of power consumption is achieved comparing with the system using fixed supply voltage.

Convergence of a global stochastic optimization algorithm with partial step size restarting

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

Convergence of a global stochastic optimization algorithm with partial step size restarting

This work develops a class of stochastic global optimization algorithms that are Kiefer-Wolfowitz (KW) type procedures with an added perturbing noise and partial step size restarting. The motivation stems from the use of KW-type procedures and Monte Carlo versions of simulated annealing algorithms in a wide range of applications. Using weak convergence approaches, our effort is directed to proving the convergence of the underlying algorithms under general noise processes.

A new approach to subband adaptive filtering

Subband adaptive filtering has attracted much attention lately. In this paper, we propose a new structure and a new formulation for adapting the filter coefficients. This structure is based on polyphase decomposition of the filter to be adapted and is independent of the type of filter banks used in the subband decomposition. The new formulation yields improved convergence rate when the LMS algorithm is used for coefficient adaptation. As we increase the number of bands in the filter, the convergence rate increases and approaches the rate that can be obtained with a flat input spectrum. The computational complexity of the proposed scheme is nearly the same as that of the fullband approach. Simulation results are included to demonstrate the efficacy of the new approach.

Nonparametric density estimation and regression achieved with topographic maps maximizing the information-theoretic entropy of their outputs

We introduce an unsupervised competitive learning rule, called the extended Maximum Entropy learning Rule (eMER), for topographic map formation. Unlike Kohonen's Self-Organizing Map (SOM) algorithm, the presence of a neighborhood function is not a prerequisite for achieving topology-preserving mappings, but instead it is intended: (1) to speed up the learning process and (2) to perform nonparametric regression. We show that, when the neighborhood function vanishes, the neural weigh t density at convergence approaches a linear function of the input density so that the map can be regarded as a nonparametric model of the input density. We apply eMER to density estimation and compare its performance with that of the SOM algorithm and the variable kernel method. Finally, we apply the ‘batch’ version of eMER to nonparametric projection pursuit regression and compare its performance with that of back-propagation learning, projection pursuit learning, constrained topolog ical mapping, and the Heskes and Kappen approach.

On the Exponential Stability of Singularly Perturbed Systems

This paper establishes some results and properties related to the exponential stability of general dynamical systems and, in particular, singularly perturbed systems. For singularly perturbed systems it is shown that if both the reduced-order system and the boundary-layer system are exponentially stable, then, provided that some further regularity conditions are satisfied, the full-order system is exponentially stable for sufficiently small values of the perturbation parameter $\mu $, and its rate of convergence approaches that of the reduced-order system $(\mu = 0)$ as $\mu $ approaches zero. Exponentially decaying norm bounds are given for the “slow” and “fast” components of the full-order system trajectories. To achieve this result, a new converse Lyapunov result for exponentially stable systems is presented.

Design optimization for structural or mechanical systems against the worst possible loads

The main objective of this paper is to point out several difficulties related to formulating and solving numerically the problem of optimal design of structural systems subjected to ‘worst admissible’ controls (that is ‘worst’ external loads). First some known facts and available results are reviewed and minor lemmas are provided so that the problem can be formulated in an appropriate mathematical setting. In the second part of the paper numerical techniques including some algorithms are discussed. Convergence and proper numerical approaches to suboptimal designs are the main topics of this part. While the main concern is structural analysis, a short digression indicates that the techniques and arguments offered here are easily extended to other applications such as the mechanical and electro-magnetic systems design.

Culture and Economy in Peripheral Europe.

The paper begins by discussing certain difficulties with neoclassical, Weberian and convergence approaches to the relationship between culture and economy. It continues with an examination of the concept of culture and its relationship to the ideas of nationalism and ethnicity. This discussion then forms the basis for an examination of the different forms that ethnicity and nationalism have taken in Mediterranean and Southeastern Europe as these areas experienced and reacted to the penetration of capitalism, and to the subsequent development of fascism and socialism. Skeptical of attempts to formulate universal theories of culture, nationalism or ethnicity, the paper proposes instead an approach which recognizes and addresses the diversity in modes of cultural organization.

Space modification: An alternative approach to chemistry problems involving geometry

AbstractThe sequential simplex optimization algorithm has been translated into non‐Euclidean space. A study of the algorithm in this space suggests that the path of convergence of the simplex depends on the properties of the space so that different convergence paths can be obtained even with the same starting points. The path of convergence approaches the path observed in Euclidean space as the metric constant of the hyperbolic space increases.

Convergence rate of a penalty-function scheme

The penalty-function approach is an attractive method for solving constrained nonlinear programming problems, since it brings into play all of the well-developed unconstrained optimization techniques, If, however, the classical steepest-descent method is applied to the standard penalty-function objective, the rate of convergence approaches zero as the penalty coefficient is increased to yield a close approximation to the true solution.