5,929 research outputs found
Pruned Bit-Reversal Permutations: Mathematical Characterization, Fast Algorithms and Architectures
A mathematical characterization of serially-pruned permutations (SPPs)
employed in variable-length permuters and their associated fast pruning
algorithms and architectures are proposed. Permuters are used in many signal
processing systems for shuffling data and in communication systems as an
adjunct to coding for error correction. Typically only a small set of discrete
permuter lengths are supported. Serial pruning is a simple technique to alter
the length of a permutation to support a wider range of lengths, but results in
a serial processing bottleneck. In this paper, parallelizing SPPs is formulated
in terms of recursively computing sums involving integer floor and related
functions using integer operations, in a fashion analogous to evaluating
Dedekind sums. A mathematical treatment for bit-reversal permutations (BRPs) is
presented, and closed-form expressions for BRP statistics are derived. It is
shown that BRP sequences have weak correlation properties. A new statistic
called permutation inliers that characterizes the pruning gap of pruned
interleavers is proposed. Using this statistic, a recursive algorithm that
computes the minimum inliers count of a pruned BR interleaver (PBRI) in
logarithmic time complexity is presented. This algorithm enables parallelizing
a serial PBRI algorithm by any desired parallelism factor by computing the
pruning gap in lookahead rather than a serial fashion, resulting in significant
reduction in interleaving latency and memory overhead. Extensions to 2-D block
and stream interleavers, as well as applications to pruned fast Fourier
transforms and LTE turbo interleavers, are also presented. Moreover,
hardware-efficient architectures for the proposed algorithms are developed.
Simulation results demonstrate 3 to 4 orders of magnitude improvement in
interleaving time compared to existing approaches.Comment: 31 page
Integer Echo State Networks: Hyperdimensional Reservoir Computing
We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed Integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The intESN
architecture is verified with typical tasks in reservoir computing: memorizing
of a sequence of inputs; classifying time-series; learning dynamic processes.
Such an architecture results in dramatic improvements in memory footprint and
computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl
Symbolic local information transfer
Recently, the permutation-information theoretic approach has been used in a
broad range of research fields. In particular, in the study of highdimensional
dynamical systems, it has been shown that this approach can be effective in
characterizing global properties, including the complexity of their
spatiotemporal dynamics. Here, we show that this approach can also be applied
to reveal local spatiotemporal profiles of distributed computations existing at
each spatiotemporal point in the system. J. T. Lizier et al. have recently
introduced the concept of local information dynamics, which consists of
information storage, transfer, and modification. This concept has been
intensively studied with regard to cellular automata, and has provided
quantitative evidence of several characteristic behaviors observed in the
system. In this paper, by focusing on the local information transfer, we
demonstrate that the application of the permutation-information theoretic
approach, which introduces natural symbolization methods, makes the concept
easily extendible to systems that have continuous states. We propose measures
called symbolic local transfer entropies, and apply these measures to two test
models, the coupled map lattice (CML) system and the Bak-Sneppen model
(BS-model), to show their relevance to spatiotemporal systems that have
continuous states.Comment: 20 pages, 7 figure
Bipartite quantum states and random complex networks
We introduce a mapping between graphs and pure quantum bipartite states and
show that the associated entanglement entropy conveys non-trivial information
about the structure of the graph. Our primary goal is to investigate the family
of random graphs known as complex networks. In the case of classical random
graphs we derive an analytic expression for the averaged entanglement entropy
while for general complex networks we rely on numerics. For large
number of nodes we find a scaling where both
the prefactor and the sub-leading O(1) term are a characteristic of
the different classes of complex networks. In particular, encodes
topological features of the graphs and is named network topological entropy.
Our results suggest that quantum entanglement may provide a powerful tool in
the analysis of large complex networks with non-trivial topological properties.Comment: 4 pages, 3 figure
Learning hard quantum distributions with variational autoencoders
Studying general quantum many-body systems is one of the major challenges in
modern physics because it requires an amount of computational resources that
scales exponentially with the size of the system.Simulating the evolution of a
state, or even storing its description, rapidly becomes intractable for exact
classical algorithms. Recently, machine learning techniques, in the form of
restricted Boltzmann machines, have been proposed as a way to efficiently
represent certain quantum states with applications in state tomography and
ground state estimation. Here, we introduce a new representation of states
based on variational autoencoders. Variational autoencoders are a type of
generative model in the form of a neural network. We probe the power of this
representation by encoding probability distributions associated with states
from different classes. Our simulations show that deep networks give a better
representation for states that are hard to sample from, while providing no
benefit for random states. This suggests that the probability distributions
associated to hard quantum states might have a compositional structure that can
be exploited by layered neural networks. Specifically, we consider the
learnability of a class of quantum states introduced by Fefferman and Umans.
Such states are provably hard to sample for classical computers, but not for
quantum ones, under plausible computational complexity assumptions. The good
level of compression achieved for hard states suggests these methods can be
suitable for characterising states of the size expected in first generation
quantum hardware.Comment: v2: 9 pages, 3 figures, journal version with major edits with respect
to v1 (rewriting of section "hard and easy quantum states", extended
discussion on comparison with tensor networks
Best Effort and Practice Activation Codes
Activation Codes are used in many different digital services and known by
many different names including voucher, e-coupon and discount code. In this
paper we focus on a specific class of ACs that are short, human-readable,
fixed-length and represent value. Even though this class of codes is
extensively used there are no general guidelines for the design of Activation
Code schemes. We discuss different methods that are used in practice and
propose BEPAC, a new Activation Code scheme that provides both authenticity and
confidentiality. The small message space of activation codes introduces some
problems that are illustrated by an adaptive chosen-plaintext attack (CPA-2) on
a general 3-round Feis- tel network of size 2^(2n) . This attack recovers the
complete permutation from at most 2^(n+2) plaintext-ciphertext pairs. For this
reason, BEPAC is designed in such a way that authenticity and confidentiality
are in- dependent properties, i.e. loss of confidentiality does not imply loss
of authenticity.Comment: 15 pages, 3 figures, TrustBus 201
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