622 research outputs found
Periodic orbits of the ensemble of Sinai-Arnold cat maps and pseudorandom number generation
We propose methods for constructing high-quality pseudorandom number
generators (RNGs) based on an ensemble of hyperbolic automorphisms of the unit
two-dimensional torus (Sinai-Arnold map or cat map) while keeping a part of the
information hidden. The single cat map provides the random properties expected
from a good RNG and is hence an appropriate building block for an RNG, although
unnecessary correlations are always present in practice. We show that
introducing hidden variables and introducing rotation in the RNG output,
accompanied with the proper initialization, dramatically suppress these
correlations. We analyze the mechanisms of the single-cat-map correlations
analytically and show how to diminish them. We generalize the Percival-Vivaldi
theory in the case of the ensemble of maps, find the period of the proposed RNG
analytically, and also analyze its properties. We present efficient practical
realizations for the RNGs and check our predictions numerically. We also test
our RNGs using the known stringent batteries of statistical tests and find that
the statistical properties of our best generators are not worse than those of
other best modern generators.Comment: 18 pages, 3 figures, 9 table
Asymptotic Bound on Binary Self-Orthogonal Codes
We present two constructions for binary self-orthogonal codes. It turns out
that our constructions yield a constructive bound on binary self-orthogonal
codes. In particular, when the information rate R=1/2, by our constructive
lower bound, the relative minimum distance \delta\approx 0.0595 (for GV bound,
\delta\approx 0.110). Moreover, we have proved that the binary self-orthogonal
codes asymptotically achieve the Gilbert-Varshamov bound.Comment: 4 pages 1 figur
Compositional Falsification of Cyber-Physical Systems with Machine Learning Components
Cyber-physical systems (CPS), such as automotive systems, are starting to
include sophisticated machine learning (ML) components. Their correctness,
therefore, depends on properties of the inner ML modules. While learning
algorithms aim to generalize from examples, they are only as good as the
examples provided, and recent efforts have shown that they can produce
inconsistent output under small adversarial perturbations. This raises the
question: can the output from learning components can lead to a failure of the
entire CPS? In this work, we address this question by formulating it as a
problem of falsifying signal temporal logic (STL) specifications for CPS with
ML components. We propose a compositional falsification framework where a
temporal logic falsifier and a machine learning analyzer cooperate with the aim
of finding falsifying executions of the considered model. The efficacy of the
proposed technique is shown on an automatic emergency braking system model with
a perception component based on deep neural networks
Adaptive Multidimensional Integration Based on Rank-1 Lattices
Quasi-Monte Carlo methods are used for numerically integrating multivariate
functions. However, the error bounds for these methods typically rely on a
priori knowledge of some semi-norm of the integrand, not on the sampled
function values. In this article, we propose an error bound based on the
discrete Fourier coefficients of the integrand. If these Fourier coefficients
decay more quickly, the integrand has less fine scale structure, and the
accuracy is higher. We focus on rank-1 lattices because they are a commonly
used quasi-Monte Carlo design and because their algebraic structure facilitates
an error analysis based on a Fourier decomposition of the integrand. This leads
to a guaranteed adaptive cubature algorithm with computational cost ,
where is some fixed prime number and is the number of data points
Optimal randomized multilevel algorithms for infinite-dimensional integration on function spaces with ANOVA-type decomposition
In this paper, we consider the infinite-dimensional integration problem on
weighted reproducing kernel Hilbert spaces with norms induced by an underlying
function space decomposition of ANOVA-type. The weights model the relative
importance of different groups of variables. We present new randomized
multilevel algorithms to tackle this integration problem and prove upper bounds
for their randomized error. Furthermore, we provide in this setting the first
non-trivial lower error bounds for general randomized algorithms, which, in
particular, may be adaptive or non-linear. These lower bounds show that our
multilevel algorithms are optimal. Our analysis refines and extends the
analysis provided in [F. J. Hickernell, T. M\"uller-Gronbach, B. Niu, K.
Ritter, J. Complexity 26 (2010), 229-254], and our error bounds improve
substantially on the error bounds presented there. As an illustrative example,
we discuss the unanchored Sobolev space and employ randomized quasi-Monte Carlo
multilevel algorithms based on scrambled polynomial lattice rules.Comment: 31 pages, 0 figure
Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions
We compare different methods for sampling from discrete probability
distributions and introduce a new algorithm which is especially efficient on
massively parallel processors, such as GPUs. The scheme preserves the
distribution properties of the input sequence, exposes constant time complexity
on the average, and significantly lowers the average number of operations for
certain distributions when sampling is performed in a parallel algorithm that
requires synchronization afterwards. Avoiding load balancing issues of na\"ive
approaches, a very efficient massively parallel construction algorithm for the
required auxiliary data structure is complemented
Applying dissipative dynamical systems to pseudorandom number generation: Equidistribution property and statistical independence of bits at distances up to logarithm of mesh size
The behavior of a family of dissipative dynamical systems representing
transformations of two-dimensional torus is studied on a discrete lattice and
compared with that of conservative hyperbolic automorphisms of the torus.
Applying dissipative dynamical systems to generation of pseudorandom numbers is
shown to be advantageous and equidistribution of probabilities for the
sequences of bits can be achieved. A new algorithm for generating uniform
pseudorandom numbers is proposed. The theory of the generator, which includes
proofs of periodic properties and of statistical independence of bits at
distances up to logarithm of mesh size, is presented. Extensive statistical
testing using available test packages demonstrates excellent results, while the
speed of the generator is comparable to other modern generators.Comment: 6 pages, 3 figures, 3 table
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