Discover how securities exchanges use matching orders to pair buy and sell orders, explore trading algorithms like FIFO and ...

Sophisticated web crawlers and extraction tools have enabled developers to harvest fresh data at scale from hundreds of ...

Every organism you have ever seen, every ecosystem you have ever walked through, is the ongoing output of an algorithm that ...

Peclinical models that have underpinned cancer research for decades often fail to predict how a drug will perform in real ...

Abstract: Recently, much progress has been made on particle swarm optimization (PSO). A number of works have been devoted to analyzing the convergence of the underlying algorithms. Nevertheless, in ...

Abstract: Greedy pursuit, which includes matching pursuit (MP) and orthogonal matching pursuit (OMP), is an efficient approach for sparse approximation. However, conventional greedy pursuit algorithms ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

The rise of artificial intelligence (AI) deep learning algorithms is helping to accelerate brain-computer interfaces (BCIs). Published in this month’s Nature Neuroscience is new research that shows ...

Non-linear regression modeling is common in epidemiology for prediction purposes or estimating relationships between predictor and response variables. Restricted cubic spline (RCS) regression is one ...

Edit distance—a classical problem in computer science—has received ongoing attention from both practitioners and theoreticians. Given two strings A and B, the edit distance is the minimum number of ...

Gaussian Approximation Potentials (GAPs) are a class of Machine Learned Interatomic Potentials routinely used to model materials and molecular systems on the atomic scale. The software implementation ...