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Domicele Jonauskaite; Lucia Camenzind; C. Alejandro Parraga; Cecile N Diouf; Mathieu Mercapide Ducommun; Lauriane Müller; Melanie Norberg; Christine Mohr |
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Colour-emotion associations in individuals with red-green colour blindness |
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2021 |
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9 |
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e11180 |
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Affect; Chromotherapy; Colour cognition; Colour vision deficiency; Cross-modal correspondences; Daltonism; Deuteranopia; Dichromatic; Emotion; Protanopia. |
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Colours and emotions are associated in languages and traditions. Some of us may convey sadness by saying feeling blue or by wearing black clothes at funerals. The first example is a conceptual experience of colour and the second example is an immediate perceptual experience of colour. To investigate whether one or the other type of experience more strongly drives colour-emotion associations, we tested 64 congenitally red-green colour-blind men and 66 non-colour-blind men. All participants associated 12 colours, presented as terms or patches, with 20 emotion concepts, and rated intensities of the associated emotions. We found that colour-blind and non-colour-blind men associated similar emotions with colours, irrespective of whether colours were conveyed via terms (r = .82) or patches (r = .80). The colour-emotion associations and the emotion intensities were not modulated by participants' severity of colour blindness. Hinting at some additional, although minor, role of actual colour perception, the consistencies in associations for colour terms and patches were higher in non-colour-blind than colour-blind men. Together, these results suggest that colour-emotion associations in adults do not require immediate perceptual colour experiences, as conceptual experiences are sufficient. |
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CIC; LAMP; 600.120; 600.128 |
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Admin @ si @ JCP2021 |
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3564 |
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Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon |
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Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform |
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2022 |
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Patterns |
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PATTERNS |
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3 |
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7 |
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100543 |
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Machine learning; data science; benchmark platform; reproducibility; competitions |
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Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. |
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June 24, 2022 |
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Science Direct |
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HuPBA |
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Admin @ si @ XEP2022 |
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3764 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Unsupervised writer style adaptation for handwritten word spotting |
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2008 |
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Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. |
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Tampa, USA |
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DAG @ dag @ RPS2008 |
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1077 |
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David Masip; Jordi Vitria |
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Boosted discriminant projections for nearest neighbor classification |
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2006 |
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Pattern Recognition, 39(2): 164–170 |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2006 |
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634 |
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Yong Xu; Jing-Yu Yang; Zhong Jin |
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A novel method for Fisher discriminant analysis |
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2004 |
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Pattern Recognition, 37(2):381–384 (IF: 2.176) |
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Admin @ si @ XYJ2004 |
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481 |
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Jian Yang; Zhong Jin; Jing-Yu Yang; David Zhang; Alejandro F. Frangi |
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Title |
Essence of kernel Fisher discriminant: KPCA plus LDA |
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2004 |
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Pattern Recognition, 37(10): 2097–2100 (IF: 2.176) |
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Admin @ si @ YJY2004 |
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480 |
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Author |
I. King; Zhong Jin |
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Integrated Probability Function and Its Application to Content-Based Image Retrieval By Relevance Feedback |
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2003 |
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Pattern Recognition, 36(9): 2177–2186 (IF: 1.611) |
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Admin @ si @ KiJ2003 |
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427 |
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X. Jing; David Zhang; Zhong Jin |
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Title |
Improvements on the uncorrelated optimal discriminant vectors |
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2003 |
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Pattern Recognition, 36(8): 1921–1923 (IF: 1.611) |
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Admin @ si @ JZJ2003a |
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428 |
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Author |
M. Bressan; David Guillamet; Jordi Vitria |
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Using an ICA Representation of Local Color Histograms for Object Recognition |
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2003 |
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Pattern Recognition, 36(3):691–701 (IF: 1.611) |
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OR;MV |
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BCNPCL @ bcnpcl @ BGV2003 |
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365 |
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Yong Xu; Jing-Yu Yang; Zhong Jin |
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Theory analysis on FSLDA and ULDA |
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2003 |
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Pattern Recognition, 36(12): 3031–3033 (IF: 1.611) |
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Admin @ si @ XYJ2003 |
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430 |
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X. Jing; David Zhang; Zhong Jin |
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Improved algorithm and generalized theory |
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2003 |
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Pattern Recognition, 36(11): 2593–2602 (IF: 1.611) |
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Admin @ si @ JZJ2003b |
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429 |
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Jordi Vitria; J. Llacer |
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Reconstructing 3D light microscopic images using the EM algorithm |
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1996 |
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Pattern Recognition Letters |
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14 |
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1491–1498 |
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BCNPCL @ bcnpcl @ ViL1996 |
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74 |
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A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
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Topological principal component analysis for face encoding and recognition |
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2001 |
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Pattern Recognition Letters |
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22 |
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6-7 |
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769–776 |
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IF: 0.552 |
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ADAS @ adas @ PVL2001 |
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155 |
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Gemma Sanchez; Josep Llados; K. Tombre |
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A mean string algorithm to compute the average among a set of 2D shapes |
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2002 |
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Pattern Recognition Letters |
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1-3 |
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203–214 |
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DAG; IF: 0.409 |
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DAG @ dag @ SLT2002 |
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A. Martinez; Jordi Vitria |
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Learning mixture models using a genetic version of the EM algorithm. |
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2000 |
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Pattern Recognition Letters |
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759–769 |
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BCNPCL @ bcnpcl @ MVi2000 |
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335 |
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