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The use of Object Oriented Programming languages and techniques for the development of Monte Carlo simulators for particle production and decay is discussed. For illustration, two specific "template" programs are presented written in Objective-C and in C++, respectively
Using a neural-network classifier we are able to separate gluon from quark jets originating from Monte Carlo–generated e+e− events with 85%–90% accuracy.
We devise a feed-forward Artificial Neural Network (ANN) procedure for predicting utility loads and present the resulting predictions for two test problems given by ``The Great Energy Predictor Shootout - The First Building Data Analysis and Prediction Competition''. Key ingredients in our approach are a method ($\delta$ -test) for determiningrelevant inputs and the Multilayer Perceptron. These me
Automated facial recognition methods have become widely used as a way to ascertain the identity of individuals. Yet the methods by which facial recognition technologies (FRT) operate – the machinic performance of the perception of the human face – are often invisible to those under their gaze. This article investigates the machinic perception of the face through an FRT method known as eigenface, i
We show that partons traversing a quark-gluon plasma can lose substantial amounts of energy also by scatterings, and not only through medium-induced radiation as mainly considered previously. Results from Monte Carlo simulations of soft interactions of partons, emerging from a hard scattering, through multiple elastic scatterings on gluons in an expanding relativistic plasma show a sizeable jet qu
The design of the 3 GeV linac for the MAX IV facility was done to provide the ability to host a future FEL in the hard X-ray as well as in the soft X-ray range. The linear accelerator, with its two bunch compressors, is now under commissioning. Through the years increasing details for the actual FEL have been discussed and presented. In parallel a steering group for the science case for a Swedish
Despite the large scientific interest on robot learning forobject picking tasks, the research on object placingis too limited. Commonly, placing is simplistically consideredas a trivial task, but real life manipulation problems indicatethe exact opposite. A placing task can have different levels ofcomplexity, ranging from the simplest tabletop placing of anobject, to more complex cases such as loa
