993 research outputs found

    Dirac Variables and Zero Modes of Gauss Constraint in Finite-Volume Two-Dimensional QED

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    The finite-volume QED1+1_{1+1} is formulated in terms of Dirac variables by an explicit solution of the Gauss constraint with possible nontrivial boundary conditions taken into account. The intrinsic nontrivial topology of the gauge group is thus revealed together with its zero-mode residual dynamics. Topologically nontrivial gauge transformations generate collective excitations of the gauge field above Coleman's ground state, that are completely decoupled from local dynamics, the latter being equivalent to a free massive scalar field theory.Comment: 13 pages, LaTe

    A Framework for Design-Time Testing of Service-Based Applications at BPEL Level

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    Software applications created on top of the service-oriented architecture (SOA) are increasingly popular but testing them remains a challenge. In this paper a framework named TASSA for testing the functional and non-functional behaviour of service-based applications is presented. The paper focuses on the concept of design time testing, the corresponding testing approach and architectural integration of the consisting TASSA tools. The individual TASSA tools with sample validation scenarios were already presented with a general view of their relation. This paper’s contribution is the structured testing approach, based on the integral use of the tools and their architectural integration. The framework is based on SOA principles and is composable depending on user requirements.The work reported in this paper was supported by a research project funded by the National Scientific Fund, Bulgarian Ministry of Education, Youth and Science, via agreement no. DOO2-182

    Direct Ascription of Missing Categorical Values in Survey Research Data

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    The complete datasets are a prerequisite for sustainable analyses, robust an- alytics and unbiased interpretation of results. Missing values in a survey occur when no data value is stored for the variable in an observation. Missing data can have a significant effect on the conclusions that can be drawn from the data. Direct ascription is the process of replacing missing data with predicted values. The aim of this work is to describe an approach to direct ascription of missing categorical values in survey research data based both on the assumption that values in a data set are missing at random and on the implementation of the correspondence analysis
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