ﻻ يوجد ملخص باللغة العربية
We analyze the time pattern of the activity of a serial killer, who during twelve years had murdered 53 people. The plot of the cumulative number of murders as a function of time is of Devils staircase type. The distribution of the intervals between murders (step length) follows a power law with the exponent of 1.4. We propose a model according to which the serial killer commits murders when neuronal excitation in his brain exceeds certain threshold. We model this neural activity as a branching process, which in turn is approximated by a random walk. As the distribution of the random walk return times is a power law with the exponent 1.5, the distribution of the inter-murder intervals is thus explained. We illustrate analytical results by numerical simulation. Time pattern activity data from two other serial killers further substantiate our analysis.
Autocatalytic networks have been used to model the emergence of self-organizing structure capable of sustaining life and undergoing biological evolution. Here, we model the emergence of cognitive structure capable of undergoing cultural evolution. Me
We discuss statistical issues in cases of serial killer nurses, focussing on the Dutch case of the nurse Lucia de Berk, arrested under suspicion of murder in 2001, convicted to life imprisonment, but declared innocent in 2010; and the case of the Eng
Recent research has found that select scientists have a disproportional share of highly cited papers. Researchers reasoned that this could not have happened if success in science was random and introduced a hidden parameter Q, or talent, to explain t
The recently proposed generalized epidemic modeling framework (GEMF) cite{sahneh2013generalized} lays the groundwork for systematically constructing a broad spectrum of stochastic spreading processes over complex networks. This article builds an algo
Computers has been endowed with a part of human-like intelligence owing to the rapid development of the artificial intelligence technology represented by the neural networks. Facing the challenge to make machines more imaginative, we consider a quant