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 ...

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 ...

Abstract: In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be ...

This paper proposes a new deep-learning-based algorithm for high-dimensional Bermudan option pricing. To the best of our knowledge, this is the first study of the arbitrary-order discretization scheme ...

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 ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

In this article, we'll get into what's actually happening at the mathematical level in quite a bit of detail, and by the end, with a little persistence, you'll come away actually understanding how it ...

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 ...