19,510 research outputs found
Neural networks, error-correcting codes, and polynomials over the binary n-cube
Several ways of relating the concept of error-correcting codes to the concept of neural networks are presented. Performing maximum-likelihood decoding in a linear block error-correcting code is shown to be equivalent to finding a global maximum of the energy function of a certain neural network. Given a linear block code, a neural network can be constructed in such a way that every codeword corresponds to a local maximum. The connection between maximization of polynomials over the n-cube and error-correcting codes is also investigated; the results suggest that decoding techniques can be a useful tool for solving such maximization problems. The results are generalized to both nonbinary and nonlinear codes
Energy efficiency of error correction on wireless systems
Since high error rates are inevitable to the wireless environment, energy-efficient error-control is an important issue for mobile computing systems. We have studied the energy efficiency of two different error correction mechanisms and have measured the efficiency of an implementation in software. We show that it is not sufficient to concentrate on the energy efficiency of error control mechanisms only, but the required extra energy consumed by the wireless interface should be incorporated as well. A model is presented that can be used to determine an energy-efficient error correction scheme of a minimal system consisting of a general purpose processor and a wireless interface. As an example we have determined these error correction parameters on two systems with a WaveLAN interfac
LT Code Design for Inactivation Decoding
We present a simple model of inactivation decoding for LT codes which can be
used to estimate the decoding complexity as a function of the LT code degree
distribution. The model is shown to be accurate in variety of settings of
practical importance. The proposed method allows to perform a numerical
optimization on the degree distribution of a LT code aiming at minimizing the
number of inactivations required for decoding.Comment: 6 pages, 7 figure
Message-Passing Inference on a Factor Graph for Collaborative Filtering
This paper introduces a novel message-passing (MP) framework for the
collaborative filtering (CF) problem associated with recommender systems. We
model the movie-rating prediction problem popularized by the Netflix Prize,
using a probabilistic factor graph model and study the model by deriving
generalization error bounds in terms of the training error. Based on the model,
we develop a new MP algorithm, termed IMP, for learning the model. To show
superiority of the IMP algorithm, we compare it with the closely related
expectation-maximization (EM) based algorithm and a number of other matrix
completion algorithms. Our simulation results on Netflix data show that, while
the methods perform similarly with large amounts of data, the IMP algorithm is
superior for small amounts of data. This improves the cold-start problem of the
CF systems in practice. Another advantage of the IMP algorithm is that it can
be analyzed using the technique of density evolution (DE) that was originally
developed for MP decoding of error-correcting codes
Quantum Error Correction with the Toric-GKP Code
We examine the performance of the single-mode GKP code and its concatenation
with the toric code for a noise model of Gaussian shifts, or displacement
errors. We show how one can optimize the tracking of errors in repeated noisy
error correction for the GKP code. We do this by examining the
maximum-likelihood problem for this setting and its mapping onto a 1D Euclidean
path-integral modeling a particle in a random cosine potential. We demonstrate
the efficiency of a minimum-energy decoding strategy as a proxy for the path
integral evaluation. In the second part of this paper, we analyze and
numerically assess the concatenation of the GKP code with the toric code. When
toric code measurements and GKP error correction measurements are perfect, we
find that by using GKP error information the toric code threshold improves from
to . When only the GKP error correction measurements are perfect
we observe a threshold at . In the more realistic setting when all error
information is noisy, we show how to represent the maximum likelihood decoding
problem for the toric-GKP code as a 3D compact QED model in the presence of a
quenched random gauge field, an extension of the random-plaquette gauge model
for the toric code. We present a new decoder for this problem which shows the
existence of a noise threshold at shift-error standard deviation for toric code measurements, data errors and GKP ancilla errors.
If the errors only come from having imperfect GKP states, this corresponds to
states with just 4 photons or more. Our last result is a no-go result for
linear oscillator codes, encoding oscillators into oscillators. For the
Gaussian displacement error model, we prove that encoding corresponds to
squeezing the shift errors. This shows that linear oscillator codes are useless
for quantum information protection against Gaussian shift errors.Comment: 50 pages, 14 figure
Survey propagation for the cascading Sourlas code
We investigate how insights from statistical physics, namely survey
propagation, can improve decoding of a particular class of sparse error
correcting codes. We show that a recently proposed algorithm, time averaged
belief propagation, is in fact intimately linked to a specific survey
propagation for which Parisi's replica symmetry breaking parameter is set to
zero, and that the latter is always superior to belief propagation in the high
connectivity limit. We briefly look at further improvements available by going
to the second level of replica symmetry breaking.Comment: 14 pages, 5 figure
An identity of Chernoff bounds with an interpretation in statistical physics and applications in information theory
An identity between two versions of the Chernoff bound on the probability a
certain large deviations event, is established. This identity has an
interpretation in statistical physics, namely, an isothermal equilibrium of a
composite system that consists of multiple subsystems of particles. Several
information--theoretic application examples, where the analysis of this large
deviations probability naturally arises, are then described from the viewpoint
of this statistical mechanical interpretation. This results in several
relationships between information theory and statistical physics, which we
hope, the reader will find insightful.Comment: 29 pages, 1 figure. Submitted to IEEE Trans. on Information Theor
Run-time Energy Management for Mobiles
Due to limited energy resources, mobile computing requires an energy-efficient a rchitecture. The dynamic nature of a mobile environment demands an architecture that allows adapting to (quickly) changing conditions. The mobile has to adapt d ynamically to new circumstances in the best suitable manner. The hardware and so ftware architecture should be able to support such adaptability and minimize the energy consumption by making resource allocation decisions at run-time. To make these decisions effective, a tradeoff has to be made between computation , communication and initialization costs (both time and energy). This paper describes our approach to construct a model that supports taking such decisions
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