A map can turn a flat table of coordinates into something you can read in seconds, and that is the real value of geospatial data visualization tools. They help you convert geographic data and ...

Provides the fast, adaptive kernel density estimator based on linear diffusion processes for one-dimensional and two-dimensional input data as outlined in the 2010 paper by Botev et al. The reference ...

Reconstructing the diverse conformations of biomolecules from cryoelectron microscopy datasets remains a longstanding challenge. Here, we present a method that surpasses current approaches across ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Two of the most widely used electronic-structure theory methods, namely, Hartree–Fock ...

Kernel density estimation (KDE) is a non-parametric method to estimate the probability density function of a random variable by taking the summation of kernel functions centered on each data point.

This paper describes a method based on a deep neural network (DNN) for estimating the number of tillers on a plant. A tiller is a branch on a grass plant, and the number of tillers is one of the most ...

cDivision of General Internal Medicine and Health Services Research, David Geffen School of Medicine at University of California, Los Angeles, CA, USA dDepartment of Epidemiology, Gillings School of ...

Abstract: We present a one-class anomaly detection method that uses time series sensor data to detect anomalies or faults in semiconductor fabrication processes. Critically, this method is trained ...

Single-molecule super-resolution microscopy (SMLM) techniques like dSTORM can reveal biological structures down to the nanometer scale. The achievable resolution is not only defined by the ...