Search

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance. [ABSTRACT FROM PUBLISHER]

Abstract In the authors paper (Štrboja et al., 2016) [42] , the notion of the bipolar pseudo-integral has been introduced. This paper extends investigations related to this new type of bipolar integrals defined with respect to ⊕-decomposable bi-capacities. We consider the main properties of the discrete bipolar pseudo-integral. A characterization theorem for the discrete bipolar pseudo-integral is proven. Monotone convergence theorems and the Fatou type lemma for the discrete bipolar pseudo-integral are considered. In order to illustrate obtained results, many interesting examples are provided. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose a novel kernel adaptive filtering algorithm, which called variable learning rates kernel adaptive filter with single feedback (SF-VLRKAF). Based on a feedback structure, the past information can be used to estimate current output to improve the filtering performance, because of a momentum term existing in the weight update equation. A switch ON–OFF normalized variable learning rate is developed to obtain a tradeoff between convergence rate and filtering accuracy of the proposed algorithm. The weights, in the SF-VLRKAF, are updated at each iteration to avoid local minimum, and the analysis of mean square convergence is performed. Furthermore, a sufficient condition ensuring mean square convergence is obtained by applying the energy conservation relation. Moreover, we derive the lower and upper bounds on a theoretical result of the steady-state excess mean square error. Simulations for nonlinear regression, chaotic time-series predictions and real-world applications are presented to illustrate the effectiveness of the new algorithm. Graphical abstract Highlights • A KAF with single feedback and variable learning rates is designed. • The mean square convergence of proposed KAF is analyzed. • A sufficient condition for convergence of proposed KAF is obtained. • Simulation results show that the proposed KAF has superior performance. [ABSTRACT FROM AUTHOR]

Shared metadata practices foster preservation and interoperability in several ways. They facilitate inter-repository exchange, the development of reusable metadata tools, and repository self-assessments and audits. Despite the benefits of shared practices, there has been little convergence on best practices for a widely used metadata strategy, PREMIS in METS. This paper proposes documenting PREMIS-METS decisions in METS profiles as a beneficial internal practice and an efficient way of sharing and comparing metadata strategies, thereby facilitating best practices. The paper then introduces a tool to help implementers document PREMIS-METS decisions in a METS profile. This tool is a checklist of 13 key PREMIS-METS issues that a repository should consider documenting in their METS profiles. Each of the 13 issues is illustrated with examples from METS profiles currently registered with the Library of Congress. [ABSTRACT FROM AUTHOR]

As a promising implementation of software systems, service composition has attracted significant attention and research, which constructs complex and value-added applications by composing existing single services to reduce the deployment time and cost. However, as the services on the Internet and the external environment are frequently changeable, these demand the service composition must be adaptive and dynamic to address these changes automatically. Therefore, this paper describes a multi-agent reinforcement learning model for the dynamic optimization of web service composition. In this model, agent can utilize reinforcement learning algorithms to interact with environment in real time to compute optimal composition strategy dynamically, and multi-agents mechanism can keep higher effectiveness in contrast to single-agent reinforcement learning. We propose a distributed Q-learning algorithm, which decompose the task into many sub-tasks and make every agent focus on own sub-task, to accelerate the convergence rate. In addition, we also introduce experience sharing strategy to improve the efficiency. As a result, these methods allow composite service to dynamically adjust itself to fit a varying environment, where the properties of the component services continue changing. Finally, a series of comparable experiments with traditional Q-learning algorithm demonstrate that our algorithms have certain validity, higher efficiency and obvious advantages. [ABSTRACT FROM AUTHOR]

The article discusses the globalization, convergence, and structural transformation. Topics mentioned include industrial growth rates, global exports, account surpluses and deficits, and stocks and flows of inward foreign direct investments as a share of global output. Other indicators examined include growth of real gross domestic product (GDP) per capital at purchasing power parity, labour productivity in the manufacturing sector, and fixed investments in selected rapidly growing countries.

Abstract: In this paper, we introduce the stickiness coefficient, a summary statistic for time-course and longitudinal data, which is designed to characterize the time dynamics of such data. The stickiness coefficient provides a simple, intuitive and informative measure that captures key information contained in time-course data. Under the assumption that the data are generated by the trajectories of a smooth underlying stochastic process, the stickiness coefficient illuminates the relationship between the value of the process at one time with the value it assumes at another time via a single numeric measure. In particular, the stickiness coefficient summarizes the extent to which deviations from the mean trajectory tend to co-vary over time. The estimation scheme we propose will allow for estimation even in the case that the longitudinal data are sparsely observed at irregular times and may be corrupted by noise. We demonstrate an estimation procedure for the stickiness coefficient and establish asymptotic consistency as well as asymptotic convergence rates. We illustrate the resulting stickiness coefficient with some theoretical calculations as well as several economic and health related data examples. [Copyright &y& Elsevier]

Profiles executive Pierre Karl Peladeau of Quebecor, Inc., and mentions the merger and acquisition strategies of his company. Acquisition of TVA television network; Creation of Quebecor Media by Peladeau, which controls newspapers, TVA, an Internet portal, and other telecommunications operations; View that Peladeau has an unusually high level of direct control over Quebecor's operations; Topic of convergence.

This comprehensive article provides an overview of the broader process of political, legal and societal changes characterizing the Baltic countries' convergence towards the European Union. The article aims to identify the specific areas and issues which reveal both similarities and differences between the three Baltic countries. Special focus has been given to issues of economic development, economic policy choices, employment, public opinion and some legal aspects. The article, first of all, tries to reveal the differences between Estonia, Latvia and Lithuania stemming from different economic policy decisions made by the Baltic countries in the 1990s as well as from to the fact that in 1997--1999 the European Union treated the Baltic countries somewhat differently in terms of conditionality. However, during the 21st century, especially due to the economic recession, the 'Baltic clocks' have been synchronized despite the obvious differences in political system and levels of economic development. The author of the current article believes that the main factor behind that development was the convergence to European Union. [ABSTRACT FROM AUTHOR]

The article discusses the future of the defense technology in the U.S. as the country sought to secure military dominance. It mentions that the country has relied on technology, in which it has been criticized as overreliance. It states that military revolution would take convergence of several innovations to move forward. It notes that the attempts for technological forecast have been disappointing, wherein results have been indecisive.

Presents information on the Canadian dollar bonds issued by the government of Quebec in the European bond market. Effect of the convergence of Canadian and U.S. economies on the province; Status of the province's pre-financing for 2006 financial year.

In the article, the author discusses the developments in the business sector in the U.S. as of early October 1999, particularly the convergence of management and staff. He cites the claim by a cohost of the television network CNN's show "Crossfire" that 60% of the American public already own stocks. He also presents the semiprivatized train system in the country, as well as the convergence of the white and African American cultures.