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Shen Yin et al.
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Jose A. Saez et al.
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Sinno Jialin Pan et al.
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Kehan Gao et al.
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P Van der Putten et al.
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R Barandela et al.
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B Schölkopf et al.
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SCIENTIFIC AMERICAN (2000)