2,898 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
California College Promise Program Characteristics and Perceptions from the Field
This report describes College Promise programs in California, including the number of College Promise programs, features, and general perceptions held by practitioners, leaders, and policymakers
Cryptography based on neural networks - analytical results
Mutual learning process between two parity feed-forward networks with
discrete and continuous weights is studied analytically, and we find that the
number of steps required to achieve full synchronization between the two
networks in the case of discrete weights is finite. The synchronization process
is shown to be non-self-averaging and the analytical solution is based on
random auxiliary variables. The learning time of an attacker that is trying to
imitate one of the networks is examined analytically and is found to be much
longer than the synchronization time. Analytical results are found to be in
agreement with simulations
Nonlocal mechanism for cluster synchronization in neural circuits
The interplay between the topology of cortical circuits and synchronized
activity modes in distinct cortical areas is a key enigma in neuroscience. We
present a new nonlocal mechanism governing the periodic activity mode: the
greatest common divisor (GCD) of network loops. For a stimulus to one node, the
network splits into GCD-clusters in which cluster neurons are in zero-lag
synchronization. For complex external stimuli, the number of clusters can be
any common divisor. The synchronized mode and the transients to synchronization
pinpoint the type of external stimuli. The findings, supported by an
information mixing argument and simulations of Hodgkin Huxley population
dynamic networks with unidirectional connectivity and synaptic noise, call for
reexamining sources of correlated activity in cortex and shorter information
processing time scales.Comment: 8 pges, 6 figure
Mutual learning in a tree parity machine and its application to cryptography
Mutual learning of a pair of tree parity machines with continuous and
discrete weight vectors is studied analytically. The analysis is based on a
mapping procedure that maps the mutual learning in tree parity machines onto
mutual learning in noisy perceptrons. The stationary solution of the mutual
learning in the case of continuous tree parity machines depends on the learning
rate where a phase transition from partial to full synchronization is observed.
In the discrete case the learning process is based on a finite increment and a
full synchronized state is achieved in a finite number of steps. The
synchronization of discrete parity machines is introduced in order to construct
an ephemeral key-exchange protocol. The dynamic learning of a third tree parity
machine (an attacker) that tries to imitate one of the two machines while the
two still update their weight vectors is also analyzed. In particular, the
synchronization times of the naive attacker and the flipping attacker recently
introduced in [1] are analyzed. All analytical results are found to be in good
agreement with simulation results
On time's arrow in Ehrenfest models with reversible deterministic dynamics
We introduce a deterministic, time-reversible version of the Ehrenfest urn
model. The distribution of first-passage times from equilibrium to
non-equilibrium states and vice versa is calculated. We find that average times
for transition to non-equilibrium always scale exponentially with the system
size, whereas the time scale for relaxation to equilibrium depends on
microscopic dynamics. To illustrate this, we also look at deterministic and
stochastic versions of the Ehrenfest model with a distribution of microscopic
relaxation times.Comment: 6 pages, 7 figures, revte
The most creative organization in the world? The BBC, 'creativity' and managerial style
The managerial styles of two BBC directors-general, John Birt and Greg Dyke, have often been contrasted but not so far analysed from the perspective of their different views of 'creative management'. This article first addresses the orthodox reading of 'Birtism'; second, it locates Dyke's 'creative' turn in the wider context of fashionable neo-management theory and UK government creative industries policy; third, it details Dyke's drive to change the BBC's culture; and finally, it concludes with some reflections on the uncertainties inherent in managing a creative organisation
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
Interplay of composition, structure, magnetism, and superconductivity in SmFeAs1-xPxO1-y
Polycrystalline samples and single crystals of SmFeAs1-xPxO1-y were
synthesized and grown employing different synthesis methods and annealing
conditions. Depending on the phosphorus and oxygen content, the samples are
either magnetic or superconducting. In the fully oxygenated compounds the main
impact of phosphorus substitution is to suppress the N\'eel temperature TN of
the spin density wave (SDW) state, and to strongly reduce the local magnetic
field in the SDW state, as deduced from muon spin rotation measurements. On the
other hand the superconducting state is observed in the oxygen deficient
samples only after heat treatment under high pressure. Oxygen deficiency as a
result of synthesis at high pressure brings the Sm-O layer closer to the
superconducting As/P-Fe-As/P block and provides additional electron transfer.
Interestingly, the structural modifications in response to this variation of
the electron count are significantly different when phosphorus is partly
substituting arsenic. Point contact spectra are well described with two
superconducting gaps. Magnetic and resistance measurements on single crystals
indicate an in-plane magnetic penetration depth of 200 nm and an anisotropy of
the upper critical field slope of 4-5. PACS number(s): 74.70.Xa, 74.62.Bf,
74.25.-q, 81.20.-nComment: 36 pages, 13 figures, 2 table
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