The amount and accuracy of passing in the game of soccer—called football across much of the world—has climbed in recent years, according to new research. The average passing volume, pass accuracy, and ...

Mathematics and computing is the study and analysis of abstract concepts, such as numbers and patterns. Mathematics is the language of choice for scientifically describing and modelling the universe ...

Applied mathematics is the application of mathematical techniques to describe real-world systems and solve technologically relevant problems. This can include the mechanics of a moving body, the ...

Abstract: We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the ...

This professional degree is offered jointly by the Department of Mathematical Sciences, the Tepper School of Business, the Department of Statistics, and the H. John Heinz III College. Admission is ...

The Master of Arts in Mathematics (MA) degree can be completed entirely online. To obtain this degree, a student needs to pass 11 courses with a GPA of 3.0 or better. One of these courses is the ...

How about making math learning fun and engaging using video content? Well, this post provides you with a carefully curated collection of some popular YouTube math channels to use in your instruction.

Learn to apply statistical methods to solve problems facing financial services industries. This course aims to present you with a wide range of mathematical ideas in a way that develops your critical ...

Abstract: Continuous-time trajectory representation has recently gained popularity for tasks where the fusion of high-frame-rate sensors and multiple unsynchronized devices is required. Lie group ...

The Great Pyramid of Giza, the oldest and sole surviving Wonder of the Ancient World, has attracted the interest of philosophers, savants, and travelers for at least four millennia. Some of this ...

STN is a powerful neural network architecture proposed by DeepMind in [1]. STN achieves real spatial invariance by automatically rectify input images before they are feeded into a normal ...

The interpolators work on a dataset of x/y points (knots). Additionally, a local regression algorithm is implemented that can be used in conjunction with the above interpolators.