Publications
Explore 921 academic publications
When to use and how to report the results of PLS-SEM
Purpose The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation m...
Very high resolution interpolated climate surfaces for global land areas
We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The c...
A Theory of Social Comparison Processes
Hypothesis I: There exists, in the human organism, a drive to evaluate his opinions and his abilities. While opinions and abilities may, at first glance, seem to be quite differ...
New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0
PhyML is a phylogeny software based on the maximum-likelihood principle. Early PhyML versions used a fast algorithm performing nearest neighbor interchanges to improve a reasona...
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
Abstract This article presents global cancer statistics by world region for the year 2022 based on updated estimates from the International Agency for Research on Cancer (IARC)....
Compressed sensing
Suppose x is an unknown vector in Ropfm (a digital image or signal); we plan to measure n general linear functionals of x and then reconstruct. If x is known to be compressible ...
A Simple, Fast, and Accurate Algorithm to Estimate Large Phylogenies by Maximum Likelihood
The increase in the number of large data sets and the complexity of current probabilistic sequence evolution models necessitates fast and reliable phylogeny reconstruction metho...
Inference from Iterative Simulation Using Multiple Sequences
The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, ho...
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...