30,536 research outputs found
New results on the genetic cryptanalysis of TEA and reduced-round versions of XTEA
Congress on Evolutionary Computation. Portland, USA, 19-23 June 2004Recently, a simple way of creating very efficient distinguishers for cryptographic primitives such as block ciphers or hash functions, was presented by the authors. Here, this cryptanalysis attack is shown to be successful when applied over reduced round versions of the block cipher XTEA. Additionally, a variant of this genetic attack is introduced and its results over TEA shown to be the most powerful published to date
The role of hyperfine mixing in semileptonic decays of doubly-heavy baryons
We analyze the effects of hyperfine mixing in semileptonic decays
of doubly heavy baryons. We qualitatively confirm the results by W. Roberts and
M. Pervin in Int. J. Mod. Phys. A, 2009, {\bf 24}: 2401-2413, finding that
mixing has a great impact on those transitions. However, predictions without
mixing differ by a factor of 2 and this discrepancy translates to the mixed
case where large differences in decay widths are observed between the two
calculations.Comment: 3 latex pages. Talk given at The 5-th International Conference on
Quarks and Nuclear Physics (QNP09), Beijing, September 200
Hyperfine mixing in semileptonic decay of doubly heavy baryons
We qualitatively corroborate the results of W. Roberts and M. Pervin in Int.
J. Mod. Phys. A 24, 2401 (2009) according to which hyperfine mixing greatly
affects the decay widths of semileptonic decays involving doubly heavy
baryons. However, our predictions for the decay widths of the unmixed
states differ from those reported in the work of Roberts and Pervin by a factor
of 2, and this discrepancy translates to the mixed case. We further show that
the predictions of heavy quark spin symmetry, might be used in the future to
experimentally extract information on the admixtures in the actual physical
baryons, in a model independent manner.Comment: 7 Latex pages, 4 Table
Dealing with Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Bayesian optimization (BO) methods are useful for optimizing functions that
are expensive to evaluate, lack an analytical expression and whose evaluations
can be contaminated by noise. These methods rely on a probabilistic model of
the objective function, typically a Gaussian process (GP), upon which an
acquisition function is built. This function guides the optimization process
and measures the expected utility of performing an evaluation of the objective
at a new point. GPs assume continous input variables. When this is not the
case, such as when some of the input variables take integer values, one has to
introduce extra approximations. A common approach is to round the suggested
variable value to the closest integer before doing the evaluation of the
objective. We show that this can lead to problems in the optimization process
and describe a more principled approach to account for input variables that are
integer-valued. We illustrate in both synthetic and a real experiments the
utility of our approach, which significantly improves the results of standard
BO methods on problems involving integer-valued variables.Comment: 7 page
Finding efficient nonlinear functions by means of genetic programming
7th International Conference, KES 2003. Proceedings, Part I. Oxford, UK, September 3-5, 2003The design of highly nonlinear functions is relevant for a number of different applications, ranging from database hashing to message authentication. But, apart from useful, it is quite a challenging task. In this work, we propose the use of genetic programming for finding functions that optimize a particular nonlinear criteria, the avalanche effect, using only very efficient operations, so that the resulting functions are extremely efficient both in hardware and in software.Supported by the Spanish Ministerio de Ciencia y Tecnologia research project
TIC2002-04498-C05-4Publicad
On the design of state-of-the-art pseudorandom number generators by means of genetic programming
Congress on Evolutionary Computation. Portland, EEUU, 19-23 June 2004The design of pseudorandom number generators by means of evolutionary computation is a classical problem. Today, it has been mostly and better accomplished by means of cellular automata and not many proposals, inside or outside this paradigm could claim to be both robust (passing all the statistical tests, including the most demanding ones) and fast, as is the case of the proposal we present here. Furthermore, for obtaining these generators, we use a radical approach, where our fitness function is not at all based in any measure of randomness, as is frequently the case in the literature, but of nonlinearity. Efficiency is assured by using only very efficient operators (both in hardware and software) and by limiting the number of terminals in the genetic programming implementation
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