Results 21 to 30 of about 1,566,699 (318)
Horticulturae, 2023 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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New Fractional Derivatives with Nonlocal and Non-Singular Kernel: Theory and Application to Heat Transfer Model [PDF]
, 2016 In this manuscript we proposed a new fractional derivative with non-local and no-singular kernel. We presented some useful properties of the new derivative and applied it to solve the fractional heat transfer model.
A. Atangana, D. Baleanu
semanticscholar +1 more source
A Kernel Method for the Two-Sample-Problem [PDF]
Neural Information Processing Systems, 2006 We propose two statistical tests to determine if two samples are from different distributions. Our test statistic is in both cases the distance between the means of the two samples mapped into a reproducing kernel Hilbert space (RKHS).
A. Gretton +4 more
semanticscholar +1 more source
Computation, 2022 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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Вавиловский журнал генетики и селекции, 2018 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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Some new Grüss inequalities associated with generalized fractional derivative
AIMS Mathematics, 2023 In this paper, we prove several new integral inequalities for the k-Hilfer fractional derivative operator, which is a fractional calculus operator. As a result, we have a whole new set of fractional integral inequalities.
Sajid Iqbal +4 more
doaj +1 more source
, 2023 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
openaire +1 more source
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network [PDF]
Computer Vision and Pattern Recognition, 2017 One of recent trends [31, 32, 14] in network architecture design is stacking small filters (e.g., 1x1 or 3x3) in the entire network because the stacked small filters is more efficient than a large kernel, given the same computational complexity. However,
Chao Peng +4 more
semanticscholar +1 more source
PERFORMANCE ENHANCEMENT OF CUDA APPLICATIONS BY OVERLAPPING DATA TRANSFER AND KERNEL EXECUTION [PDF]
Applied Computer Science, 2021 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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On Hermitian separability of the next-to-leading order BFKL kernel for the adjoint representation of the gauge group in the planar N = 4 SYM [PDF]
, 2015 We analyze a modification of the BFKL kernel for the adjoint representation of the colour group in the maximally supersymmetric (N=4) Yang-Mills theory in the limit of a large number of colours, related to the modification of the eigenvalues of the ...
Fadin, V. S., Fiore, R.
core +2 more sources