Abstract Sequential X-ray photon correlation spectroscopy (XPCS) reveals sample dynamics by analyzing a series of coherent scattering images, which is often time-consuming. For applications like real-time XPCS analysis, high efficiency desired. Pixel binning straightforward strategy to reduce the processing time, but over-binning may result in an insufficient signal-to-noise ratio. In this work...

The goal of metagenomic binning is to reconstruct genomes from a mixture DNA sequences into genomic bins, which can be considered clustering task. Multiple methods have been proposed for this task, such as distance-based metrics, machine learning, and ensemble approaches. We propose BinChill, method, based on the generic co-occurrence ensembler ACE. BinChill incorporates domain information in f...

We consider the problem of the interpolation of irregularly spaced spatial data, applied to observation of Cosmic Microwave Background (CMB) anisotropies. The well-known interpolation methods and kriging are compared to the binning method which serves as a reference approach. We analyse kriging versus binning results for different resolutions and noise level in the original data. Most of the ti...

In this paper the influence of intensity clustering and shading correction on mutual information based image registration is studied. Instead of the generally used equidistant re-binning, we use k-means clustering in order to achieve a more natural binning of the intensity distribution. Secondly, image inhomogeneities occurring notably in MR images can have adverse effects on the registration. ...

We give a mathematical framework for weighted ensemble (WE) sampling, a binning and resampling technique for efficiently computing probabilities in molecular dynamics. We prove that WE sampling is unbiased in a very general setting that includes adaptive binning. We show that when WE is used for stationary calculations in tandem with a coarse model, the coarse model can be used to optimize the ...

Traditional discretization techniques for machine learning, from examples with continuous feature spaces, are not efficient when the data is in the form of a stream from an unknown, possibly changing, distribution. We present a time-and-memory-efficient discretization technique based on computing ε-approximate exponential frequency quantiles, and prove bounds on the worst-case error introduced ...

Metagenomics is the study of microbial communities sampled directly from their natural environment, without prior culturing. Among the computational tools recently developed for metagenomic sequence analysis, binning tools attempt to classify the sequences in a metagenomic dataset into different bins (i.e., species), based on various DNA composition patterns (e.g., the tetramer frequencies) of ...