Abstract: Side-channel techniques, such as Distance-of-Means and Kolmogorov-Smirnov analysis, provide valuable insights about leakages from the indirect perspectives of statistical moment and ...

Sampling from probability distributions with known density functions (up to normalization) is a fundamental challenge across various scientific domains. From Bayesian uncertainty quantification to ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. The application presented here utilizes the R Shiny platform to ...

An extension to numpy using discrete fourier transforms to compute the curl of 2D and 3D functions. This produces results far more accurate than using 10th-order finite difference derivatives (which ...

Department of Basic Science, Nippon Veterinary and Life Science University, Tokyo, Japan. According to the uncertainty principle, the motion of the single electron in the 1s orbit of a hydrogen atom ...

The Riemann hypothesis is the most important open question in number theory—if not all of mathematics. It has occupied experts for more than 160 years. And the problem appeared both in mathematician ...

What Is A Probability Density Function? A probability density function, also known as a bell curve, is a fundamental statistics concept, that describes the likelihood of a continuous random variable ...

dxxx(x,) returns the density or the value on the y-axis of a probability distribution for a discrete value of x pxxx(q,) returns the cumulative density function (CDF) or the area under the curve to ...