9 research outputs found
Extracting the Kolmogorov Complexity of Strings and Sequences from Sources with Limited Independence
An infinite binary sequence has randomness rate at least if, for
almost every , the Kolmogorov complexity of its prefix of length is at
least . It is known that for every rational , on
one hand, there exists sequences with randomness rate that can not be
effectively transformed into a sequence with randomness rate higher than
and, on the other hand, any two independent sequences with randomness
rate can be transformed into a sequence with randomness rate higher
than . We show that the latter result holds even if the two input
sequences have linear dependency (which, informally speaking, means that all
prefixes of length of the two sequences have in common a constant fraction
of their information). The similar problem is studied for finite strings. It is
shown that from any two strings with sufficiently large Kolmogorov complexity
and sufficiently small dependence, one can effectively construct a string that
is random even conditioned by any one of the input strings
Von Neumann Normalisation of a Quantum Random Number Generator
In this paper we study von Neumann un-biasing normalisation for ideal and
real quantum random number generators, operating on finite strings or infinite
bit sequences. In the ideal cases one can obtain the desired un-biasing. This
relies critically on the independence of the source, a notion we rigorously
define for our model. In real cases, affected by imperfections in measurement
and hardware, one cannot achieve a true un-biasing, but, if the bias "drifts
sufficiently slowly", the result can be arbitrarily close to un-biasing. For
infinite sequences, normalisation can both increase or decrease the
(algorithmic) randomness of the generated sequences. A successful application
of von Neumann normalisation---in fact, any un-biasing transformation---does
exactly what it promises, un-biasing, one (among infinitely many) symptoms of
randomness; it will not produce "true" randomness.Comment: 27 pages, 2 figures. Updated to published versio
Projection Theorems Using Effective Dimension
In this paper we use the theory of computing to study fractal dimensions of projections in Euclidean spaces. A fundamental result in fractal geometry is Marstrand\u27s projection theorem, which shows that for every analytic set E, for almost every line L, the Hausdorff dimension of the orthogonal projection of E onto L is maximal.
We use Kolmogorov complexity to give two new results on the Hausdorff and packing dimensions of orthogonal projections onto lines. The first shows that the conclusion of Marstrand\u27s theorem holds whenever the Hausdorff and packing dimensions agree on the set E, even if E is not analytic. Our second result gives a lower bound on the packing dimension of projections of arbitrary sets. Finally, we give a new proof of Marstrand\u27s theorem using the theory of computing
The Benefits of Entropy in Population Protocols
A distributed computing system can be viewed as the result of the interplay between a distributed algorithm specifying the effects of a local event (e.g. reception of a message), and an adversary choosing the interleaving (schedule) of these events in the execution. In the context of large networks of mobile pairwise interacting agents (population protocols), the adversary models the mobility of the agents by choosing the successive pairs of interacting agents. For some problems, assuming that the adversary selects the schedule according to some probability distribution greatly helps to devise (almost) correct solutions. But how much randomness is really necessary? To what extent does a problem admit implementations that are robust against a "not so random" schedule? This paper takes a first step in addressing this question by borrowing the concept of T-randomness, 0 <= T <= 1, from algorithmic information theory. Roughly speaking, the value T fixes the entropy rate of the considered schedules. For instance, the case T = 1 corresponds, in a specific sense, to schedules in which the pairs of interacting agents are chosen independently and uniformly (perfect randomness). The holy grail question can then be precisely stated as determining the optimal entropy rate to solve a given problem. We first show that perfect randomness is never required. Precisely, if a finite-state algorithm solves a problem with 1-randomness, then this algorithm still solves the same problem with T-randomness for some T < 1. Second, we illustrate how to compute bounds on the optimal entropy rate of a specific problem, namely the leader election problem
Kolmogorov complexity and computably enumerable sets
We study the computably enumerable sets in terms of the: (a) Kolmogorov
complexity of their initial segments; (b) Kolmogorov complexity of finite
programs when they are used as oracles. We present an extended discussion of
the existing research on this topic, along with recent developments and open
problems. Besides this survey, our main original result is the following
characterization of the computably enumerable sets with trivial initial segment
prefix-free complexity. A computably enumerable set is -trivial if and
only if the family of sets with complexity bounded by the complexity of is
uniformly computable from the halting problem
Proceedings of the 26th International Symposium on Theoretical Aspects of Computer Science (STACS'09)
The Symposium on Theoretical Aspects of Computer Science (STACS) is held alternately in France and in Germany. The conference of February 26-28, 2009, held in Freiburg, is the 26th in this series. Previous meetings took place in Paris (1984), Saarbr¨ucken (1985), Orsay (1986), Passau (1987), Bordeaux (1988), Paderborn (1989), Rouen (1990), Hamburg (1991), Cachan (1992), W¨urzburg (1993), Caen (1994), M¨unchen (1995), Grenoble (1996), L¨ubeck (1997), Paris (1998), Trier (1999), Lille (2000), Dresden (2001), Antibes (2002), Berlin (2003), Montpellier (2004), Stuttgart (2005), Marseille (2006), Aachen (2007), and Bordeaux (2008). ..