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Consider a collection of n independent non-linear birth
Consider a collection of n independent non-linear birth

Summary of the papers on ”Increasing risk” by Rothschild and Stiglitz
Summary of the papers on ”Increasing risk” by Rothschild and Stiglitz

... Than there exist sequences Fn and Gn such that Fn → F , Gn → G and for each n, Gn could have been obtained from Fn by a finite number of MPS’s. This theorem results from the two partial results: the first lemma proves it for simple step functions with a finite number of steps and the other one is conce ...
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Bayesian updating of mechanical models - Application in fracture mechanics

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Hardware random number generator



In computing, a hardware random number generator (TRNG, True Random Number Generator) is an apparatus that generates random numbers from a physical process, rather than a computer program. Such devices are often based on microscopic phenomena that generate low-level, statistically random ""noise"" signals, such as thermal noise, the photoelectric effect, and other quantum phenomena. These processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is obtained. The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely. They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL).Random number generators can also be built from ""random"" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use. Although dice have been mostly used in gambling, and in more recent times as ""randomizing"" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce a limited number of random bits per second. In order to increase the data rate, they are often used to generate the ""seed"" for a faster Cryptographically secure pseudorandom number generator, which then generates the pseudorandom output sequence.
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