Analysing Errors of Open Information Extraction Systems

We report results on benchmarking Open Information Extraction (OIE) systems using RelVis, a toolkit for benchmarking Open Information Extraction systems. Our comprehensive benchmark contains three data sets from the news domain and one data set from Wikipedia with overall 4522 labeled sentences and 11243 binary or n-ary OIE relations. In our analysis on these data sets we compared the performance of four popular OIE systems, ClausIE, OpenIE 4.2, Stanford OpenIE and PredPatt. In addition, we evaluated the impact of five common error classes on a subset of 749 n-ary tuples. From our deep analysis we unreveal important research directions for a next generation of OIE systems.Comment: Accepted at Building Linguistically Generalizable NLP Systems at EMNLP 201

Deep convolutional and LSTM recurrent neural networks for multimodal wearable activity recognition

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation

Numerical modelling of grinding in a stirred media mill: Hydrodynamics and collision characteristics

Producing nanoparticles in dense suspensions can be achieved in a stirred media mill. However the mechanisms of fragmentation in the mill are still not fully understood and the process remains laborious because of the large amount of supplied energy. We focus on the numerical analysis of the local hydrodynamics in the mill. Based on the flow simulations we determine the parameters which control the efficiency of the collisions between grinding beads (impact velocities and orientation of the collisions). The suspension flow (grinding beads, particles, carrying fluid) is modelled with effective physical properties. We solve directly the continuity and Navier–Stokes equations for the equivalent fluid assuming that the flow is two-dimensional and steady. Depending on the Reynolds number and the non-Newtonian behaviour of the fluid, we found that the flow is composed of several toroidal vortices. The most energetic collisions are driven by the strong shear experienced by the suspension within the gap between the disc tip and the wall chamber

Zur Ressourcenproduktivität von spurgeführten Hochgeschwindigkeitsverkehrssystemen: Ein Vergleich von ICE und Transrapid. Eine gemeinsame Studie des Lehrstuhls für Technikwirkungs- und Innovationsforschung der Universität GH Kassel und des Wuppertal-Instituts

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A novel optical sensing lab-on-a-disc platform for chromium speciation

The determination chromium speciation in the field is a significant analytical challenge. While chromium exists in oxidation states from 0 to VI, it is predominantly found in the (III) and (VI) states [1]. Industry effluent (e.g. textile/electroplating) is a common source of chromium pollution in the environment. Due to corrosion inhibitors used in pipes, and contamination leaching from sanitary landfills, drinking water supplies can become contaminated also [2]. The bioavailability and toxicity of chromium is largely dependent the oxidation state of the element [2]. Consumption of Cr (III) is an essential component in human diet, as it is responsible for maintaining glucose, lipid and protein metabolism [3]. In contrast, Cr (VI) is strongly oxidizing, exhibiting high toxicity, with carcinogenic and mutagenic properties [4]. It is recommended by the World Health Organisation (WHO) that the maximum allowable concentration of chromium (VI) in drinking water is 0.05 mg L−1 [5]. Handheld colourimeters for on-site measurements are a convenient option for frequent water monitoring; however the limit of detection (LOD) of these devices is typically higher than the recommended limit. Microfluidic ‘lab-on-a-disc’ technologies were used in the development of an optical sensor for chromium speciation in water. The principal behind these devices is to minimize laboratory processes onto a microfluidic system that can be brought to the sampling site for rapid sample-to-answer analyses. The objective for this device was to design and fabricate a fully integrated optical sensor for on-site measurement of both trivalent and hexavalent chromium in freshwater. A strong focus was placed on maximizing sensitivity in order to achieve a low LOD