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In particular, our approach outperforms previous methods in terms of preserving small structures.
The experimental results indicate that our approach outperforms previous deployment methods significantly in terms of service response time.
Various experiments in comparison with state-of-the-art techniques have shown that our approach outperforms previous methods in boundary evaluation of both trivial and complicated CSG with massive faces while maintaining high robustness.
Our approach outperforms previous methods in terms of accuracy of prognosis prediction.
Moreover, our approach outperforms previous models generated on the GDSC dataset.
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The evaluation results show that both models from this new approach outperform previous spatial models in predicting future criminal incidents.
An experimental evaluation on synthetic and real-life event logs shows that this discover-and-structure approach consistently outperforms previous approaches with respect to a range of accuracy and complexity measures.
The results demonstrate that the adaptive multi-scale approach outperforms the previous fixed multi-scale approach and is capable of producing better than state of the art performance compared to existing robotic navigation algorithms.
Furthermore, our flow outperforms previous approaches with Speculative FUs.
The results show that, with minimal resources, hybrid imaging outperforms previous approaches to the motion blur problem.
Our experimental results show that our proposal outperforms previous approaches in terms of accuracy and flexibility.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com