Termination Analysis by Learning Terminating Programs

We present a novel approach to termination analysis. In a first step, the analysis uses a program as a black-box which exhibits only a finite set of sample traces. Each sample trace is infinite but can be represented by a finite lasso. The analysis can "learn" a program from a termination proof for the lasso, a program that is terminating by construction. In a second step, the analysis checks that the set of sample traces is representative in a sense that we can make formal. An experimental evaluation indicates that the approach is a potentially useful addition to the portfolio of existing approaches to termination analysis

Human strategies in translation and interpreting : what MT can learn from translators

Translation - which we think of as a broader concept above written translation as well as interpreting - is basically a complex decision process. The decisions are based on available information. Translation problems arise when the translator does not have necessary information available at the moment of the translation. This is where translation strategies come into effect, which translators use consciously or subconsciously. We think that both forms of translation use basically the same type of strategies, which are, however, not easy to detect or to measure. Furthermore, we think that the model of translation as a decision process also applies to machine translation. In our paper, we try to prove this using the example of reduction as a translation strategy. Reduction is used both in written translation and in interpreting, but is more prominent in the latter. In our work, we focus upon dialogue interpreting, a non-simultaneous type used in face-to-face interactions. We try to outline how reduction strategies could be modelled in a machine interpreting system (such as VERBMOBIL), using the concept of the target of translation

Xanthines as a scaffold for molecular diversity

Summary: Xanthines represent a new, versatile scaffold for combinatorial chemistry. A five-step solid-phase synthesis of xanthine derivatives is described which includes alkylations, a nucleophilic displacement reaction at a heterocycle and a ring closure reaction by condensation of a nitroso function with an activated methylene group. The selected reaction sequence allows the production of a highly diverse small-molecule combinatorial compound librar

Power matters: Foucault’s pouvoir/savoir as a conceptual lens in information research and practice

© the author, 2015. Introduction. This paper advocates Foucault's notion of pouvoir/savoir (power/knowledge) as a conceptual lens that information researchers might fruitfully use to develop a richer understanding of the relationship between knowledge and power. Methods. Three of the authors’ earlier studies are employed to illustrate the use of this conceptual lens. Methodologically, the studies are closely related: they adopted a qualitative research design and made use of semi-structured and/or conversational, in-depth interviews as their primary method of data collection. The data were analysed using an inductive, discourse analytic approach. Analysis. The paper provides a brief introduction to Foucault’s concept before examining the information practices of academic, professional and artistic communities. Through concrete empirical examples, the authors aim to demonstrate how a Foucauldian lens will provide a more in-depth understanding of how particular information practices exert authority in a discourse community while other such practices may be construed as ineffectual. Conclusion. The paper offers a radically different conceptual lens through which researchers can study information practices, not in individual or acultural terms but as a social construct, both a product and a generator of power/knowledge

Power matters: The importance of foucault’s power/knowledge as a conceptual lens in km research and practice

© Emerald Group Publishing Limited. Purpose - The purpose of this paper is to engage knowledge management (KM) researchers and practitioners with Foucault’s power/knowledge lens as a way of thinking about and recognising the central role of power in organisational knowledge cultures. Design/methodology/approach - The empirical illustrations in this paper are drawn from two qualitative studies in different professional and institutional contexts (insurance and theatre work). Both studies used in-depth interviews and discourse analysis as their principal methods of data collection and analysis. Findings - The empirical examples illustrate how practitioners operate within complex power/knowledge relations that shape their practices of knowledge sharing, generation and use. The findings show how an application of the power/knowledge lens renders visible both the constraining and productive force of power in KM. Research limitations/implications - Researchers may apply the conceptual tools presented here in a wider variety of institutional and professional contexts to examine the complex and multifaceted role of power in a more in-depth way. Practical implications - KM professionals will benefit from an understanding of organisational power/knowledge relations when seeking to promote transformational changes in their organisations and build acceptance for KM initiatives. Originality/value - This paper addresses a gap in the literature around theoretical and empirical discussions of power as well as offering an alternative to prevailing resource-based views of power in KM

A Multispectral Light Field Dataset and Framework for Light Field Deep Learning

Deep learning undoubtedly has had a huge impact on the computer vision community in recent years. In light field imaging, machine learning-based applications have significantly outperformed their conventional counterparts. Furthermore, multi- and hyperspectral light fields have shown promising results in light field-related applications such as disparity or shape estimation. Yet, a multispectral light field dataset, enabling data-driven approaches, is missing. Therefore, we propose a new synthetic multispectral light field dataset with depth and disparity ground truth. The dataset consists of a training, validation and test dataset, containing light fields of randomly generated scenes, as well as a challenge dataset rendered from hand-crafted scenes enabling detailed performance assessment. Additionally, we present a Python framework for light field deep learning. The goal of this framework is to ensure reproducibility of light field deep learning research and to provide a unified platform to accelerate the development of new architectures. The dataset is made available under dx.doi.org/10.21227/y90t-xk47 . The framework is maintained at gitlab.com/iiit-public/lfcnn