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Graphene has shown superior performance in various electronic devices such as electrochemical supercapacitors.
Both algorithms were chosen as they have shown superior performance in the MIREX campaigns in recent years.
The direct urea-hydrogen peroxide fuel cell (DUHPFC) with the Ni2Mo1/G anode catalysts has also shown superior performance.
Recently, unsupervised feature learning and deep learning methods have shown superior performance in learning efficient representation of images at multiple layers [24, 28 33].
We have compared it with recent state-of-the-art methods, and the results have shown superior performance of our proposed method.
These approaches, such as kernel descriptors (KD) and efficient kernel descriptors (EKD), have shown superior performance than pre-defined image features (e.g., SIFT or HoG) in object recognition.
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Overall, each of the classifiers performed better than background, with Random Forests showing superior performance of ∼75% accuracy for repressed and nonrepressed genes.
The results show superior performance of this algorithm when applied to various modulation schemes.
The OCR shows superior performance compared to the rest of the rules.
All results show superior performance compared to three state-of-the-art trackers.
In all cases the NF + OA shows superior performance than pure water.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com