4,120 research outputs found
Secure exchange of information by synchronization of neural networks
A connection between the theory of neural networks and cryptography is
presented. A new phenomenon, namely synchronization of neural networks is
leading to a new method of exchange of secret messages. Numerical simulations
show that two artificial networks being trained by Hebbian learning rule on
their mutual outputs develop an antiparallel state of their synaptic weights.
The synchronized weights are used to construct an ephemeral key exchange
protocol for a secure transmission of secret data. It is shown that an opponent
who knows the protocol and all details of any transmission of the data has no
chance to decrypt the secret message, since tracking the weights is a hard
problem compared to synchronization. The complexity of the generation of the
secure channel is linear with the size of the network.Comment: 11 pages, 5 figure
Statistical mechanical aspects of joint source-channel coding
An MN-Gallager Code over Galois fields, , based on the Dynamical Block
Posterior probabilities (DBP) for messages with a given set of autocorrelations
is presented with the following main results: (a) for a binary symmetric
channel the threshold, , is extrapolated for infinite messages using the
scaling relation for the median convergence time, ;
(b) a degradation in the threshold is observed as the correlations are
enhanced; (c) for a given set of autocorrelations the performance is enhanced
as is increased; (d) the efficiency of the DBP joint source-channel coding
is slightly better than the standard gzip compression method; (e) for a given
entropy, the performance of the DBP algorithm is a function of the decay of the
correlation function over large distances.Comment: 6 page
Parallel vs. Sequential Belief Propagation Decoding of LDPC Codes over GF(q) and Markov Sources
A sequential updating scheme (SUS) for belief propagation (BP) decoding of
LDPC codes over Galois fields, , and correlated Markov sources is
proposed, and compared with the standard parallel updating scheme (PUS). A
thorough experimental study of various transmission settings indicates that the
convergence rate, in iterations, of the BP algorithm (and subsequently its
complexity) for the SUS is about one half of that for the PUS, independent of
the finite field size . Moreover, this 1/2 factor appears regardless of the
correlations of the source and the channel's noise model, while the error
correction performance remains unchanged. These results may imply on the
'universality' of the one half convergence speed-up of SUS decoding
Judicial Review of Antitakeover Devices Employed In the Noncoercive Tender Offer Context: Making Sense of the Unocal Test
Judicial Review of Antitakeover Devices Employed In the Noncoercive Tender Offer Context: Making Sense of the Unocal Test
Generation of unpredictable time series by a Neural Network
A perceptron that learns the opposite of its own output is used to generate a
time series. We analyse properties of the weight vector and the generated
sequence, like the cycle length and the probability distribution of generated
sequences. A remarkable suppression of the autocorrelation function is
explained, and connections to the Bernasconi model are discussed. If a
continuous transfer function is used, the system displays chaotic and
intermittent behaviour, with the product of the learning rate and amplification
as a control parameter.Comment: 11 pages, 14 figures; slightly expanded and clarified, mistakes
corrected; accepted for publication in PR
America's Giving Challenge: Assessment and Reflection Report
Provides an overview of Case's 2007-08 online Giving Challenge and its impact on grassroots fundraising. Outlines the types of participating causes, charities, and donors; characteristics of successful efforts; lessons learned; and recommendations
America's Giving Challenge 2009: Assessment & Reflection Report
Based on a survey of participants and "conversational case studies," outlines lessons learned about social media outreach and giving contests, such as winners' common attributes, fundraisers' online media adoption, and best practices in vetting contests
Sublattice synchronization of chaotic networks with delayed couplings
Synchronization of chaotic units coupled by their time delayed variables are
investigated analytically. A new type of cooperative behavior is found:
sublattice synchronization. Although the units of one sublattice are not
directly coupled to each other, they completely synchronize without time delay.
The chaotic trajectories of different sublattices are only weakly correlated
but not related by generalized synchronization. Nevertheless, the trajectory of
one sublattice is predictable from the complete trajectory of the other one.
The spectra of Lyapunov exponents are calculated analytically in the limit of
infinite delay times, and phase diagrams are derived for different topologies
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