Vampire Getting Noisy: Will Random Bits Help Conquer Chaos? (System Description)

System Description: Vampire 1.0

About Random Bits

1 Random Bit Generators 2 1.1 Pseudo-Random Bit Generators . . . . . . . . . . . . . . . . . . . . 2 2 Physical Sources for Random Bits 3 2.1 Exploiting Air Turbulence in Hard Disk Drives . . . . . . . . . . 3 2.2 Generating Unbiased Random Bits . . . . . . . . . . . . . . . . . . 4 3 Generating Random Seeds 5 3.1 Obtaining Random Timings . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Locati...

Image Recovery from Noisy Transmission using Soft Bits and Markov Random Field Models

Abstract In this paper, a new method for robust image recovery from noisy transmission based on softbits and Markov random field modeling is proposed. The method aims to exploit the residual redundancy present in the symbols produced by a source encoder together with knowledge about the statistical properties of natural images. The soft-bit information required for reconstruction may either be ...

Products and Help Bits in Decision Trees

We investigate two problems concerning the complexity of evaluating a function f at a k-tuple of unrelated inputs by k parallel decision tree algorithms. In the product problem, for some xed depth bound d, we seek to maximize the fraction of input k-tuples for which all k decision trees are correct. Assume that for a single input to f, the best decision tree algorithm of depth d is correct on a...

On Learning to Coordinate: Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms

A mere bounded number of random bits judiciously employed by a probabilistically correct algorithmic coordinator is shown to increase the power of learning to coordinate compared to deterministic algorithmic coordinators. Furthermore, these probabilistic algorithmic coordinators are provably not characterized in power by teams of deterministic ones. An insightful, enumeration technique based, n...