Search results for "Neural Networks"

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…

Energy saving in WWTP: Daily benchmarking under uncertainty and data availability limitations

2016

Efficient management of Waste Water Treatment Plants (WWTPs) can produce significant environmental and economic benefits. Energy benchmarking can be used to compare WWTPs, identify targets and use these to improve their performance. Different authors have performed benchmark analysis on monthly or yearly basis but their approaches suffer from a time lag between an event, its detection, interpretation and potential actions. The availability of on-line measurement data on many WWTPs should theoretically enable the decrease of the management response time by daily benchmarking. Unfortunately this approach is often impossible because of limited data availability. This paper proposes a methodolo…

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.

Bone Fusion in Normal and Pathological Development is Constrained by the Network Architecture of the Human Skull

2016

The premature fusion of cranial bones, craniosynostosis, affects the correct development of the skull producing morphological malformations in newborns. To assess the susceptibility of each craniofacial articulation to close prematurely, we used a network model of the skull to quantify the link reliability (an index based on stochastic block modeling and Bayesian inference) of each articulation. We show that, of the 93 human skull articulations at birth, the few articulations that are associated with nonsyndromic craniosynostosis conditions have statistically significant lower reliability scores than the others. In a similar way, articulations that close during the normal postnatal developm…

Quantifying and Processing Biomedical and Behavioral Signals

2019

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.