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Cryogen-free low-temperature setups are becoming more prominent in experimental science due to their convenience and reliability, and concern about the increasing scarcity of helium as a natural resource. Despite not having any moving parts at the cold end, pulse tube cryocoolers introduce vibrations that can be detrimental to the experiments. We characterize the coupling of these vibrations to the electrical signal observed on cables installed in a cryogen-free dilution refrigerator. The dominant electrical noise is in the 5-10 kHz range and its magnitude is found to be strongly temperature dependent. We test the performance of different cables designed to diagnose and tackle the noise, and find triboelectrics to be the dominant mechanism coupling the vibrations to the electrical signal. Flattening a semi-rigid cable or jacketing a flexible cable in order to restrict movement within the cable, successfully reduces the noise level by over an order of magnitude. Furthermore, we characterize the effect of the pulse tube vibrations on an electron spin qubit device in this setup. Coherence measurements are used to map out the spectrum of the noise experienced by the qubit, revealing spectral components matching the spectral signature of the pulse tube.

Abstract. We present a question answering system architecture which processes natural language questions in a pipeline consisting of five steps: i) question parsing and query template generation, ii) lookup in an inverted index, iii) string similarity computation, iv) lookup in a lexical database in order to find synonyms, and v) semantic similarity computation. These steps are ordered with respect to their computational effort, following the idea of layered processing: questions are passed on along the pipeline only if they cannot be answered on the basis of earlier processing steps, thereby invoking computationally expensive operations only for complex queries that require them. In this paper we present an evaluation of the system on the dataset provided by the 2nd Open Challenge on Question Answering over Linked Data (QALD-2). The main, novel contribution is a systematic empirical investigation of the impact of the single processing components on the overall performance of question answering over linked data.

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