13,034 research outputs found
A ferrofluid based neural network: design of an analogue associative memory
We analyse an associative memory based on a ferrofluid, consisting of a
system of magnetic nano-particles suspended in a carrier fluid of variable
viscosity subject to patterns of magnetic fields from an array of input and
output magnetic pads. The association relies on forming patterns in the
ferrofluid during a trainingdphase, in which the magnetic dipoles are free to
move and rotate to minimize the total energy of the system. Once equilibrated
in energy for a given input-output magnetic field pattern-pair the particles
are fully or partially immobilized by cooling the carrier liquid. Thus produced
particle distributions control the memory states, which are read out
magnetically using spin-valve sensors incorporated in the output pads. The
actual memory consists of spin distributions that is dynamic in nature,
realized only in response to the input patterns that the system has been
trained for. Two training algorithms for storing multiple patterns are
investigated. Using Monte Carlo simulations of the physical system we
demonstrate that the device is capable of storing and recalling two sets of
images, each with an accuracy approaching 100%.Comment: submitted to Neural Network
Entrograms and coarse graining of dynamics on complex networks
Using an information theoretic point of view, we investigate how a dynamics
acting on a network can be coarse grained through the use of graph partitions.
Specifically, we are interested in how aggregating the state space of a Markov
process according to a partition impacts on the thus obtained lower-dimensional
dynamics. We highlight that for a dynamics on a particular graph there may be
multiple coarse grained descriptions that capture different, incomparable
features of the original process. For instance, a coarse graining induced by
one partition may be commensurate with a time-scale separation in the dynamics,
while another coarse graining may correspond to a different lower-dimensional
dynamics that preserves the Markov property of the original process. Taking
inspiration from the literature of Computational Mechanics, we find that a
convenient tool to summarise and visualise such dynamical properties of a
coarse grained model (partition) is the entrogram. The entrogram gathers
certain information-theoretic measures, which quantify how information flows
across time steps. These information theoretic quantities include the entropy
rate, as well as a measure for the memory contained in the process, i.e., how
well the dynamics can be approximated by a first order Markov process. We use
the entrogram to investigate how specific macro-scale connection patterns in
the state-space transition graph of the original dynamics result in desirable
properties of coarse grained descriptions. We thereby provide a fresh
perspective on the interplay between structure and dynamics in networks, and
the process of partitioning from an information theoretic perspective. We focus
on networks that may be approximated by both a core-periphery or a clustered
organization, and highlight that each of these coarse grained descriptions can
capture different aspects of a Markov process acting on the network.Comment: 17 pages, 6 figue
Emergent Predication Structure in Hidden State Vectors of Neural Readers
A significant number of neural architectures for reading comprehension have
recently been developed and evaluated on large cloze-style datasets. We present
experiments supporting the emergence of "predication structure" in the hidden
state vectors of these readers. More specifically, we provide evidence that the
hidden state vectors represent atomic formulas where is a
semantic property (predicate) and is a constant symbol entity identifier.Comment: Accepted for Repl4NLP: 2nd Workshop on Representation Learning for
NL
The Kinetic Basis of Morphogenesis
It has been shown recently (Shalygo, 2014) that stationary and dynamic
patterns can arise in the proposed one-component model of the analog
(continuous state) kinetic automaton, or kinon for short, defined as a
reflexive dynamical system with active transport. This paper presents
extensions of the model, which increase further its complexity and tunability,
and shows that the extended kinon model can produce spatio-temporal patterns
pertaining not only to pattern formation but also to morphogenesis in real
physical and biological systems. The possible applicability of the model to
morphogenetic engineering and swarm robotics is also discussed.Comment: 8 pages. Submitted to the 13th European Conference on Artificial Life
(ECAL-2015) on March 10, 2015. Accepted on April 28, 201
Hierarchical Bin Buffering: Online Local Moments for Dynamic External Memory Arrays
Local moments are used for local regression, to compute statistical measures
such as sums, averages, and standard deviations, and to approximate probability
distributions. We consider the case where the data source is a very large I/O
array of size n and we want to compute the first N local moments, for some
constant N. Without precomputation, this requires O(n) time. We develop a
sequence of algorithms of increasing sophistication that use precomputation and
additional buffer space to speed up queries. The simpler algorithms partition
the I/O array into consecutive ranges called bins, and they are applicable not
only to local-moment queries, but also to algebraic queries (MAX, AVERAGE, SUM,
etc.). With N buffers of size sqrt{n}, time complexity drops to O(sqrt n). A
more sophisticated approach uses hierarchical buffering and has a logarithmic
time complexity (O(b log_b n)), when using N hierarchical buffers of size n/b.
Using Overlapped Bin Buffering, we show that only a single buffer is needed, as
with wavelet-based algorithms, but using much less storage. Applications exist
in multidimensional and statistical databases over massive data sets,
interactive image processing, and visualization
Multiscale models of colloidal dispersion of particles in nematic liquid crystals
We use homogenization theory to develop a multiscale model of colloidal dispersion of particles in nematic liquid crystals under weak-anchoring conditions. We validate the model by comparing it with simulations by using the Landau–de Gennes free energy and show that the agreement is excellent. We then use the multiscale model to study the effect that particle anisotropy has on the liquid crystal: spherically symmetric particles always reduce the effective elastic constant. Asymmetric particles introduce an effective alignment field that can increase the Fredericks threshold and decrease the switch-off time
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