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 $8m$ and $m$ is odd. We achieve this by proving the equivalent lifting property for the class of central simple algebras of degree $8m$ with involution. A companion paper ([S3]) deals with the case of $m$, $2m$ and $4m$ 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 $ABC$ 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