14,294 research outputs found
Diffusion and Correlations in Lattice Gas Automata
We present an analysis of diffusion in terms of the spontaneous density
fluctuations in a non-thermal two-species fluid modeled by a lattice gas
automaton. The power spectrum of the density correlation function is computed
with statistical mechanical methods, analytically in the hydrodynamic limit,
and numerically from a Boltzmann expression for shorter time and space scales.
In particular we define an observable -- the weighted difference of the species
densities -- whose fluctuation correlations yield the diffusive mode
independently of the other modes so that the corresponding power spectrum
provides a measure of diffusion dynamics solely. Automaton simulations are
performed to obtain measurements of the spectral density over the complete
range of wavelengths (from the microscopic scale to the macroscopic scale of
the automaton universe). Comparison of the theoretical results with the
numerical experiments data yields the following results: (i) the spectral
functions of the lattice gas fluctuations are in accordance with those of a
classical `non-thermal' fluid; (ii) the Landau-Placzek theory, obtained as the
hydrodynamic limit of the Boltzmann theory, describes the spectra correctly in
the long wavelength limit; (iii) at shorter wavelengths and at moderate
densities the complete Boltzmann theory provides good agreement with the
simulation data. These results offer convincing validation of lattice gas
automata as a microscopic approach to diffusion phenomena in fluid systems.Comment: 9 pages (revtex source), 12 Postscript figure
Generic algebras with involution of degree 8m
The centers of the generic central simple algebras with involution are
interesting objects in the theory of central simple algebras. These fields also
arise as invariant fields for linear actions of projective orthogonal or
symplectic groups. In this paper, we prove that when the characteristic is not
2, these fields are retract rational, in the case the degree is and is
odd. We achieve this by proving the equivalent lifting property for the class
of central simple algebras of degree with involution. A companion paper
([S3]) deals with the case of , and where stronger rationality
results are proven.Comment: 7 page
Model and simulation of a solar kiln with energy storage
A solar kiln with energy storage can be used for continuous drying. This kiln consisted of several units which were modeled to simulate it in operation. A model was proposed for each unit, and another based on laboratory tests for drying a wooden board by passing air across. These models were combined to produce a global model. Simulation results were then analyzed and showed that the use of storage was justified to reduce drying time. Moreover, with the judicious use of storage and air renewal, drying schedules could be produced for a better quality of dried wood
Solar timber kilns: State of the art and foreseeable developments
Analysis of the evolution in solar heated drying kilns in recent decades shows that there have been a series of modifications to optimize their thermal and drying efficiency. Using an analysis method based on product design, we report on existing solar timber kilns. The dryers and their component units are studied, developments are noted, focusing on changing trends in technological systems. As a result of this analysis we suggest some future adaptations
Training deep neural networks with low precision multiplications
Multipliers are the most space and power-hungry arithmetic operators of the
digital implementation of deep neural networks. We train a set of
state-of-the-art neural networks (Maxout networks) on three benchmark datasets:
MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats:
floating point, fixed point and dynamic fixed point. For each of those datasets
and for each of those formats, we assess the impact of the precision of the
multiplications on the final error after training. We find that very low
precision is sufficient not just for running trained networks but also for
training them. For example, it is possible to train Maxout networks with 10
bits multiplications.Comment: 10 pages, 5 figures, Accepted as a workshop contribution at ICLR 201
An oriented-design simplified model for the efficiency of a flat plate solar air collector
In systems design, suitably adapted physical models are required. Different modelling approaches for a solar air collector were studied in this paper. First, a classical model was produced, based on a linearization of the conservation of energy equations. Its resolution used traditional matrix methods. In order to improve the possibilities for use in design, the behaviour of the collector was next expressed in terms of efficiency. Lastly, simplified models constructed from the results obtained with the classical linearized model, and explicitly including the design variables of the collector, were proposed. These reduced models were then evaluated in terms of Parsimony, Exactness, Precision and Specialisation (PEPS). It was concluded that one of them (D2), using a low number of variables and of equations, is well suited for the design of solar air collector coupled with other sub-systems in more complex devices such as solar kiln with energy storag
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide
range of tasks, with the best results obtained with large training sets and
large models. In the past, GPUs enabled these breakthroughs because of their
greater computational speed. In the future, faster computation at both training
and test time is likely to be crucial for further progress and for consumer
applications on low-power devices. As a result, there is much interest in
research and development of dedicated hardware for Deep Learning (DL). Binary
weights, i.e., weights which are constrained to only two possible values (e.g.
-1 or 1), would bring great benefits to specialized DL hardware by replacing
many multiply-accumulate operations by simple accumulations, as multipliers are
the most space and power-hungry components of the digital implementation of
neural networks. We introduce BinaryConnect, a method which consists in
training a DNN with binary weights during the forward and backward
propagations, while retaining precision of the stored weights in which
gradients are accumulated. Like other dropout schemes, we show that
BinaryConnect acts as regularizer and we obtain near state-of-the-art results
with BinaryConnect on the permutation-invariant MNIST, CIFAR-10 and SVHN.Comment: Accepted at NIPS 2015, 9 pages, 3 figure
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
Lattice gas automaton approach to "Turbulent Diffusion"
A periodic Kolmogorov type flow is implemented in a lattice gas automaton.
For given aspect ratios of the automaton universe and within a range of
Reynolds number values, the averaged flow evolves towards a stationary
two-dimensional type flow. We show the analogy between the streamlines of
the flow in the automaton and the phase plane trajectories of a dynamical
system. In practice flows are commonly studied by seeding the fluid with
suspended particles which play the role of passive tracers. Since an actual
flow is time-dependent and has fluctuations, the tracers exhibit interesting
intrinsic dynamics. When tracers are implemented in the automaton and their
trajectories are followed, we find that the tracers displacements obey a
diffusion law, with ``super-diffusion'' in the direction orthogonal to the
direction of the initial forcing.Comment: 7 revtex4 pages including 3 figure
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