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Current issue

ELEKTRO 7/2020 was released on June 24th 2020. Its digital version will be available on July 24th 2020.

Topic: Cables, conductors and cable engineering

Main Article
New traction power supply technology 25 kV/50 Hz (part 2)

SVĚTLO (Light) 3/2020 was released on June 8th 2020. Its digital version will be available on July 8th 2020.

Professional organizations activities
Announcement: LUMEN V4 2020 is cancelled
What is new in CIE, April 2020

Accessories of lighting installations
Foxtrot as a “Master Control” in Hotel Breukelen
Lighting regulators – control of lighting on the constant level

Algorithm quickly simulates a roll of loaded dice

1. 6. 2020 | MIT | www.mit.edu

The fast and efficient generation of random numbers has long been an important challenge. For centuries, games of chance have relied on the roll of a die, the flip of a coin, or the shuffling of cards to bring some randomness into the proceedings. In the second half of the 20th century, computers started taking over that role, for applications in cryptography, statistics, and artificial intelligence, as well as for various simulations — climatic, epidemiological, financial, and so forth.

MIT researchers have now developed a computer algorithm that might, at least for some tasks, churn out random numbers with the best combination of speed, accuracy, and low memory requirements available today. The algorithm, called the Fast Loaded Dice Roller (FLDR) is a computer program that simulates the roll of dice to produce random integers. The dice can have any number of sides, and they are “loaded,” or weighted, to make some sides more likely to come up than others. A loaded die can still yield random numbers — as one cannot predict in advance which side will turn up — but the randomness is constrained to meet a preset probability distribution.

Random numbers generation

FLDR, of course, is still brand new and has not yet seen widespread use. But its developers are already thinking of ways to improve its effectiveness through both software and hardware engineering. They also have specific applications in mind, apart from the general, ever-present need for random numbers. Where FLDR can help most is by making so-called Monte Carlo simulations and Monte Carlo inference techniques more efficient. Just as FLDR uses coin flips to simulate the more complicated roll of weighted, many-sided dice, Monte Carlo simulations use a dice roll to generate more complex patterns of random numbers.

Read more at MIT

Image Credit: Unsplash

-jk-