514 research outputs found
A two step algorithm for learning from unspecific reinforcement
We study a simple learning model based on the Hebb rule to cope with
"delayed", unspecific reinforcement. In spite of the unspecific nature of the
information-feedback, convergence to asymptotically perfect generalization is
observed, with a rate depending, however, in a non- universal way on learning
parameters. Asymptotic convergence can be as fast as that of Hebbian learning,
but may be slower. Moreover, for a certain range of parameter settings, it
depends on initial conditions whether the system can reach the regime of
asymptotically perfect generalization, or rather approaches a stationary state
of poor generalization.Comment: 13 pages LaTeX, 4 figures, note on biologically motivated stochastic
variant of the algorithm adde
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
On-Line Learning with Restricted Training Sets: An Exactly Solvable Case
We solve the dynamics of on-line Hebbian learning in large perceptrons
exactly, for the regime where the size of the training set scales linearly with
the number of inputs. We consider both noiseless and noisy teachers. Our
calculation cannot be extended to non-Hebbian rules, but the solution provides
a convenient and welcome benchmark with which to test more general and advanced
theories for solving the dynamics of learning with restricted training sets.Comment: 19 pages, eps figures included, uses epsfig macr
Training a perceptron in a discrete weight space
On-line and batch learning of a perceptron in a discrete weight space, where
each weight can take different values, are examined analytically and
numerically. The learning algorithm is based on the training of the continuous
perceptron and prediction following the clipped weights. The learning is
described by a new set of order parameters, composed of the overlaps between
the teacher and the continuous/clipped students. Different scenarios are
examined among them on-line learning with discrete/continuous transfer
functions and off-line Hebb learning. The generalization error of the clipped
weights decays asymptotically as / in the case of on-line learning with binary/continuous activation
functions, respectively, where is the number of examples divided by N,
the size of the input vector and is a positive constant that decays
linearly with 1/L. For finite and , a perfect agreement between the
discrete student and the teacher is obtained for . A crossover to the generalization error ,
characterized continuous weights with binary output, is obtained for synaptic
depth .Comment: 10 pages, 5 figs., submitted to PR
Storage capacity of correlated perceptrons
We consider an ensemble of single-layer perceptrons exposed to random
inputs and investigate the conditions under which the couplings of these
perceptrons can be chosen such that prescribed correlations between the outputs
occur. A general formalism is introduced using a multi-perceptron costfunction
that allows to determine the maximal number of random inputs as a function of
the desired values of the correlations. Replica-symmetric results for and
are compared with properties of two-layer networks of tree-structure and
fixed Boolean function between hidden units and output. The results show which
correlations in the hidden layer of multi-layer neural networks are crucial for
the value of the storage capacity.Comment: 16 pages, Latex2
Multilayer neural networks with extensively many hidden units
The information processing abilities of a multilayer neural network with a
number of hidden units scaling as the input dimension are studied using
statistical mechanics methods. The mapping from the input layer to the hidden
units is performed by general symmetric Boolean functions whereas the hidden
layer is connected to the output by either discrete or continuous couplings.
Introducing an overlap in the space of Boolean functions as order parameter the
storage capacity if found to scale with the logarithm of the number of
implementable Boolean functions. The generalization behaviour is smooth for
continuous couplings and shows a discontinuous transition to perfect
generalization for discrete ones.Comment: 4 pages, 2 figure
Unconventional MBE Strategies from Computer Simulations for Optimized Growth Conditions
We investigate the influence of step edge diffusion (SED) and desorption on
Molecular Beam Epitaxy (MBE) using kinetic Monte-Carlo simulations of the
solid-on-solid (SOS) model. Based on these investigations we propose two
strategies to optimize MBE growth. The strategies are applicable in different
growth regimes: During layer-by-layer growth one can exploit the presence of
desorption in order to achieve smooth surfaces. By additional short high flux
pulses of particles one can increase the growth rate and assist layer-by-layer
growth. If, however, mounds are formed (non-layer-by-layer growth) the SED can
be used to control size and shape of the three-dimensional structures. By
controlled reduction of the flux with time we achieve a fast coarsening
together with smooth step edges.Comment: 19 pages, 7 figures, submitted to Phys. Rev.
Ecofeminism in the 21st Century
This paper considers the influence of ecofeminism on policy concerning gender (in)equality and the environment during the past 20 years. It reviews the broad contours of the ecofeminist debate before focusing on the social construction interpretation of women's relationship with the environment. It will argue that there have been substantial policy shifts in Europe and the UK in both the environmental and equalities fields, and that this is in part a result of lobbying at a range of scales by groups informed by ecofeminist debates. Nevertheless, the paper cautions that these shifts are largely incremental and operate within existing structures, which inevitably limit their capacity to create change. As policy addresses some of the concerns highlighted by ecofeminism, academic discourse and grass roots activity have been moving on to address other issues, and the paper concludes with a brief consideration of contemporary trajectories of ecofeminism and campaigning on issues that link women's, feminist and environment concerns
Breaking the Bluetooth Pairing â The Fixed Coordinate Invalid Curve Attack
Bluetooth is a widely deployed standard for wireless communications between mobile devices. It uses authenticated Elliptic Curve Diffie-Hellman for its key exchange. In this paper we show that the authentication provided by the Bluetooth pairing protocols is insufficient and does not provide the promised MitM protection. We present a new attack that modifies the y-coordinates of the public keys (while preserving the x-coordinates). The attack compromises the encryption keys of all of the current Bluetooth authenticated pairing protocols, provided both paired devices are vulnerable. Specifically, it successfully compromises the encryption keys of 50% of the Bluetooth pairing attempts, while in the other 50% the pairing of the victims is terminated. The affected vendors have been informed and patched their products accordingly, and the Bluetooth specification had been modified to address the new attack. We named our new attack the âFixed Coordinate Invalid Curve Attackâ. Unlike the well known âInvalid Curve Attackâ of Biehl et. al. which recovers the private key by sending multiple specially crafted points to the victim, our attack is a MitM attack which modifies the public keys in a way that lets the attacker deduce the shared secret
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