Search results for " NEURAL NETWORKS"
showing 10 items of 390 documents
Simulation of Models for Isotropic and Anisotropic Orientational Glasses
1992
“Orientational glass” behavior is found when molecular crystals are randomly diluted, and quadrupole moments get frozen by random alignment of the molecules, similar to “spin glass” behavior of randomly diluted magnets. Monte Carlo simulation of lattice models where quadrupole moments interact with nearest neighbor Gaussian coupling is a unique tool to study this behavior. The time-dependent glass order parameter exhibits anomalously slow relaxation, compatible with the Kohlrausch-Williams-Watts (KWW) stretched exponential function. Both isotropic and anisotropic models exhibit in d=2 and d=3 spatial dimensions glass transitions at zero temperature only. While the glass correlation length a…
Recent advances in the development of holey optical fibers based on sulfide glasses
2006
International audience; Microstructured optical fibers as new optical objects have been developed in the recent past years, firstly from silica glass and then from other oxide glasses such as tellurite or different heavy cations oxide glasses. However very few results have been reported concerning non-oxide glasses and more particularly chalcogenide glasses. In a photonic crystal fiber the arrangement of air holes along the transverse section of the fiber around a solid glassy core leads to unique optical properties, such as for example broadband single-mode guidance, adjustable dispersion, nonlinear properties. Since the effective modal area is adjustable thanks to geometrical parameters, …
SPATIAL MULTIFRACTALITY OF ELECTRONIC STATES AND THE METAL-INSULATOR TRANSITION IN DISORDERED SYSTEMS
1993
For the investigation of the spatial behavior of electronic wave functions in disordered systems, we employ the Anderson model of localization. The eigenstates of the corresponding Hamiltonian are calculated numerically by means of the Lanczos algorithm and are analyzed with respect to their spatial multifractal properties. We find that the wave functions show spatial multifractality for all parameter cases not too far away from the metal-insulator transition (MIT) which separates localized from extended states in this model. Exactly at the MIT, multifractality is expected to exist on all length scales larger than the lattice spacing. It is found that the corresponding singularity spectrum…
Nonexponential 2H spin-lattice relaxation as a signature of the glassy state
1990
Abstract High-precision measurements of 2H spin-lattice relaxation on several molecular glass-forming liquids have been performed. As a general feature the following can be stated: At temperatures more than ten to twenty degrees above the calorimetric glass transition temperature Tg the 2H spin-lattice relaxation is exponential; below that temperature regime the relaxation is nonexponential. This crossover from exponential to nonexponential magnetization recovery implies that no common spin temperature caused by spin diffusion exists in a 2H glass. This contrasts 1H spin-lattice relaxation which is found to be strictly monoexponential throughout. The occurrence of nonexponential 2H relaxati…
Artificial neural networks for predicting dorsal pressures on the foot surface while walking
2012
In this work, artificial neural networks (ANNs) are proposed to predict the dorsal pressure over the foot surface exerted by the shoe upper while walking. A model that is based on the multilayer perceptron (MLP) is used since it can provide a single equation to model the exerted pressure for all the materials used as shoe uppers. Five different models are produced, one model for each one of the four subjects under study and an overall model for the four subjects. The inputs to the neural model include the characteristics of the material and the positions during a whole step of 14 pressure sensors placed on the foot surface. The goal is to find models with good generalization capabilities, (…
The tensor of interaction of a two-level system with an arbitrary strain field
2007
The interaction between two-level systems (TLS) and strain fields in a solid is contained in the diagonal matrix element of the interaction hamiltonian, $\delta$, which, in general, has the expression $\delta=2[\gamma]:[S]$, with the tensor $[\gamma]$ describing the TLS ``deformability'' and $[S]$ being the symmetric strain tensor. We construct $[\gamma]$ on very general grounds, by associating to the TLS two objects: a direction, $\hat\bt$, and a forth rank tensor of coupling constants, $[[R]]$. Based on the method of construction and on the invariance of the expression of $\delta$ with respect to the symmetry transformation of the solid, we conclude that $[[R]]$ has the same structure as …
Energy landscape properties studied using symbolic sequences
2006
We investigate a classical lattice system with $N$ particles. The potential energy $V$ of the scalar displacements is chosen as a $\phi ^4$ on-site potential plus interactions. Its stationary points are solutions of a coupled set of nonlinear equations. Starting with Aubry's anti-continuum limit it is easy to establish a one-to-one correspondence between the stationary points of $V$ and symbolic sequences $\bm{\sigma} = (\sigma_1,...,\sigma_N)$ with $\sigma_n=+,0,-$. We prove that this correspondence remains valid for interactions with a coupling constant $\epsilon$ below a critical value $\epsilon_c$ and that it allows the use of a ''thermodynamic'' formalism to calculate statistical prope…
Modified mode-coupling theory for the collective dynamics of simple liquids
2011
Recently it has been shown that mode-coupling theory, which accounts for the salient features of glassy relaxation near the liquid–glass transition, is also capable of describing the collective excitations of simple liquids away from the glass transition. In order to further improve the agreement between theory and computer simulations on Lennard-Jones argon we modify MCT by taking binary collisions into account. This, in fact, improves the agreement. We also show that multiplying the memory function of the original theory with a reduction factor leads to similar results.
Deep Learning Architectures for DNA Sequence Classification
2016
DNA sequence classification is a key task in a generic computational framework for biomedical data analysis, and in recent years several machine learning technique have been adopted to successful accomplish with this task. Anyway, the main difficulty behind the problem remains the feature selection process. Sequences do not have explicit features, and the commonly used representations introduce the main drawback of the high dimensionality. For sure, machine learning method devoted to supervised classification tasks are strongly dependent on the feature extraction step, and in order to build a good representation it is necessary to recognize and measure meaningful details of the items to cla…
Hierarchically nested factor model from multivariate data
2005
We show how to achieve a statistical description of the hierarchical structure of a multivariate data set. Specifically we show that the similarity matrix resulting from a hierarchical clustering procedure is the correlation matrix of a factor model, the hierarchically nested factor model. In this model, factors are mutually independent and hierarchically organized. Finally, we use a bootstrap based procedure to reduce the number of factors in the model with the aim of retaining only those factors significantly robust with respect to the statistical uncertainty due to the finite length of data records.