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The main difference between the effects of single and multiple scattering is that single scattering can usually be treated as a random phenomenon, whereas multiple scattering, somewhat counterintuitively, can be modeled as a more deterministic process because the combined results of a large number of scattering events tend to average out.
This section discusses strategies for reducing the problem of multiclass classification to multiple binary classification problems. It can be categorized into one vs rest and one vs one. The techniques developed based on reducing the multi-class problem into multiple binary problems can also be called problem transformation techniques.
The formulation of multi-label learning was first introduced by Shen et al. in the context of Semantic Scene Classification, [1] [2] and later gained popularity across various areas of machine learning. Formally, multi-label classification is the problem of finding a model that maps inputs x to binary vectors y; that is, it assigns a value of 0 ...
This program calculates the scattering, absorption, and attenuation parameters, as well as the angular scattering patterns of a single coated sphere according to Aden-Kerker theory. 2007 L. Liu, H. Wang, B. Yu, Y. Xu, J. Shen [15] C: Unknown Light scattering by a coated sphere (extinction efficiency, scattering efficiency, light scattering ...
The code accounts all important multiple-scattering paths with user-specified precision and is able to fit a single structure model to a set of EXAFS spectra, acquired at several absorption edges. [ 30 ] [ 31 ] [ 32 ] The evolutionary algorithm is used for optimization allowing more efficient exploration of the possible configuration space with ...
Depending on the type and variation in training data, machine learning can be roughly categorized into three frameworks: supervised learning, unsupervised learning, and reinforcement learning. Multiple instance learning (MIL) falls under the supervised learning framework, where every training instance has a label, either discrete or real valued ...
Interferometric scattering microscopy (iSCAT) refers to a class of methods that detect and image a subwavelength object by interfering the light scattered by it with a reference light field. The underlying physics is shared by other conventional interferometric methods such as phase contrast or differential interference contrast , or reflection ...
The integration of single-cell multi-omic data presents different challenges depending on whether the datasets are matched or unmatched. [48] Matched datasets refer to multiple omic layers that are measured from the same individual cell whereas unmatched data refer to dataset that are measured from a different set of cells.