In order to find the minimizer of Ⅼ using gradient descent with fixed stepsize, we create a function called gd. This function takes the arguments: start, f, gradient, step_size, maxiter, and tolerance ...

Abstract: Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to ...

Already registered? Click here to login now. Linear electromagnetic devices — such as linear motors, generators, actuators, and magnetic gears — play a vital role in precision motion control, energy ...

1 MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom 2 Department of Psychology and York Biomedical Research Institute, University of York, York, United Kingdom ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Implementations of matrix multiplication via diffusion and reactions, thus eliminating ...

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ABSTRACT: Purpose: To introduce a practical method of using an Electron Density Phantom (EDP) to evaluate different dose calculation algorithms for photon beams in a treatment planning system (TPS) ...

Standard computer implementations of Dantzig's simplex method for linear programming are based upon forming the inverse of the basic matrix and updating the inverse ...

Catharina Capitain and Melanie Schüßler from the Faculty of Geosciences at the University of Tübingen, Tübingen, Germany describe a novel approach using matrix-matched semiquantification to ...

A windowed sinc function can implement a low-pass filter, and a two-dimensional convolutional filter can blur or sharpen images. In part 3 of this series, we introduced a low-pass filter based on the ...