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

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

Improved Separation of Polyphonic Chamber Music Signals by Integrating Instrument Activity Labels

The separation of music signals is a very challenging task, especially in case of polyphonic chamber music signals because of the similar frequency ranges and sound characteristics of the different instruments to separate. In this work, a joint separation approach in the time domain with a U-Net architecture is extended to incorporate additional time-dependent instrument activity information for improved instrument track extractions. Different stages are investigated to integrate the additional information, but an input before the deepest encoder block achieves best separation results as well as highest robustness against randomly wrong labels. This approach outperforms a label integration by multiplication and the input of a static instrument label. Targeted data augmentation by incoherent mixtures is used for a trio example of violin, trumpet, and flute to improve separation results. Moreover, an alternative separation approach with one independent separation model for each instrument is investigated, which enables a more flexible architecture. In this case, an input after the deepest encoder block achieves best separation results, but the robustness is slightly reduced compared to the joint model. The improvements by additional information on active instruments are verified by using real instrument activity predictions for both the joint and the independent separation approaches