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A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets)
Hedi Xia, Hector D. Ceniceros
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Genome-wide association analysis reveals a novel QTL CsPC1 for pericarp color in cucumber
Background Cucumber is an important melon crop in the world, with different pericarp colors. However, the candidate genes and the underlying genetic mechanism for such an important trait in cucumber are unknown.
Hongyu Huang +11 more
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Background: The most significant motivations for designing multi-biometric systems are high-accuracy recognition, high-security assurances as well as overcoming the limitations like non-universality, noisy sensor data, and large intra-user variations ...
Mohammad H Safavipour +2 more
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The effects of twelve different pre-drying and drying methods on the chemical composition in the pericarp and kernel of Amomum tsao-ko were studied. The volatile components were isolated from the samples by simultaneous distillation and extraction and ...
Hui-wei Qin +7 more
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The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation.
Elies Ramon +6 more
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This study was carried out to determine the fatty acid composition, bioactive compounds, and mineral element content of standard hazelnut cultivars and accessions from the Eastern Black Sea region.
Mehmet Yaman +6 more
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Sunflower broomrape is a parasitic chlorophyll plant that affects the root system of the host plant, absorbing water, nutrients and toxic products from it. Germination of broomrape seeds occurs due to strigolactones released into the soil by the roots of
S. G. Hablak +2 more
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This study aims at developing models in analyzing the results of proficiency testing (PT) schemes for a limited number of participants. The models can determine the best estimators of location and dispersion using unsatisfactory results as a criterion by
Dimitris Tsamatsoulis
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This chapter introduces a powerful class of machine learning approaches called kernel methods, which present an alternative to arguably more widely known neural network approaches. Kernel methods can learn even highly nonlinear problems by making an implicit transformation from a low-dimensional input space into a higher-dimensional feature space. This
Pinheiro Jr, Max, Dral, Pavlo
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PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTION [PDF]
The CPU-GPU combination is a widely used heterogeneous computing system in which the CPU and GPU have different address spaces. Since the GPU cannot directly access the CPU memory, prior to invoking the GPU function the input data must be available on ...
K. RAJU, Niranjan N CHIPLUNKAR
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