Search results for "Neural Networks"

Recent Advances in Complex Networks Theories with Applications

2014

Disorder-induced single-mode transmission.

2017

Localized states trap waves propagating in a disordered potential and play a crucial role in Anderson localization, which is the absence of diffusion due to disorder. Some localized states are barely coupled with neighbours because of differences in wavelength or small spatial overlap, thus preventing energy leakage to the surroundings. This is the same degree of isolation found in the homogeneous core of a single-mode optical fibre. Here we show that localized states of a disordered optical fibre are single mode: the transmission channels possess a high degree of resilience to perturbation and invariance with respect to the launch conditions. Our experimental approach allows identification…

Wind speed spatial estimation for energy planning in Sicily: A neural kriging application

2008

Abstract One of the first steps for the exploitation of any energy source is necessarily represented by its estimation and mapping at the aim of identifying the most suitable areas in terms of energy potential. In the field of renewable energies this is often a very difficult task, because the energy source is in this case characterized by relevant variations over space and time. This implies that any temporal, but also spatial, estimation model has to be able to incorporate this spatial and temporal variability. The paper deals with the spatial estimation of the wind fields in Sicily (Italy) by following a data-driven approach. Starting from the results of a preliminary study, a novel tech…

Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs.

2021

Background: Palliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patient9s frailty are usual dimensions to decide PC inclusion. Objectives: The main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting palliative care decision making: one-year mortality, survival regression and one-year frailty classification. Methods: The dataset used in this stud…

Defects in glasses

1995

Abstract The absence of long range order in the glass structure allows to define only point defects in these materials. They are: 1) intrinsic defects—atomic size local deviation from short range order; 2) impurity defects—isolated impurity atoms or ions in the glass network; 3) intrinsic impurity defects—complexes consisting of the impurity atoms chemically bonded to one of the intrinsic defect atoms. The latter defects are characteristic for the doped glasses. Presence of point defects in glasses introduces new spectroscopic properties of these solid materials. Defect generation, interaction and recombination reactions resulting from the external influence causes the glass spectroscopic p…

Long-Term Behavioral Programming Induced by Peripuberty Stress in Rats Is Accompanied by GABAergic-Related Alterations in the Amygdala

2014

Stress during childhood and adolescence is a risk factor for psychopathology. Alterations in γ-aminobutyric acid (GABA), the main inhibitory neurotransmitter in the brain, have been found following stress exposure and fear experiences and are often implicated in anxiety and mood disorders. Abnormal amygdala functioning has also been detected following stress exposure and is also implicated in anxiety and social disorders. However, the amygdala is not a unitary structure; it includes several nuclei with different functions and little is known on the potential differences the impact of early life stress may have on this system within different amygdaloid nuclei. We aimed here to evaluate pote…

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

2020

 To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…

A Novel Systolic Parallel Hardware Architecture for the FPGA Acceleration of Feedforward Neural Networks

2019

New chips for machine learning applications appear, they are tuned for a specific topology, being efficient by using highly parallel designs at the cost of high power or large complex devices. However, the computational demands of deep neural networks require flexible and efficient hardware architectures able to fit different applications, neural network types, number of inputs, outputs, layers, and units in each layer, making the migration from software to hardware easy. This paper describes novel hardware implementing any feedforward neural network (FFNN): multilayer perceptron, autoencoder, and logistic regression. The architecture admits an arbitrary input and output number, units in la…

Antimicrobial Activity Characterization in a Heterogeneous Group of Compounds

1998

In this work we carry out a study of pattern recognition to detect the microbiological activity in a group of heterogeneous compounds. The structural descriptors utilized are the topological connectivity indexes. The methods followed are stepwise linear discriminant analysis (linear analysis) and artificial neural network (nonlinear analysis). Although both methods are appropriate to differentiate between active and inactive compounds, the artificial neural network is, in this case, more adequate, since it shows in a test set a prediction success of 98%, versus 92% obtained with linear discriminant analysis.

Spin Glasses on Thin Graphs

1995

In a recent paper we found strong evidence from simulations that the Isingantiferromagnet on ``thin'' random graphs - Feynman diagrams - displayed amean-field spin glass transition. The intrinsic interest of considering such random graphs is that they give mean field results without long range interactions or the drawbacks, arising from boundary problems, of the Bethe lattice. In this paper we reprise the saddle point calculations for the Ising and Potts ferromagnet, antiferromagnet and spin glass on Feynman diagrams. We use standard results from bifurcation theory that enable us to treat an arbitrary number of replicas and any quenched bond distribution. We note the agreement between the f…