Search results for "NETWORKS"
showing 10 items of 3260 documents
Fractional differential calculus for 3D mechanically based non-local elasticity
2011
This paper aims to formulate the three-dimensional (3D) problem of non-local elasticity in terms of fractional differential operators. The non-local continuum is framed in the context of the mechanically based non-local elasticity established by the authors in a previous study; Non-local interactions are expressed in terms of central body forces depending on the relative displacement between non-adjacent volume elements as well as on the product of interacting volumes. The non-local, long-range interactions are assumed to be proportional to a power-law decaying function of the interaction distance. It is shown that, as far as an unbounded domain is considered, the elastic equilibrium proble…
Advances in photonic reservoir computing
2017
We review a novel paradigm that has emerged in analogue neuromorphic optical computing. The goal is to implement a reservoir computer in optics, where information is encoded in the intensity and phase of the optical field. Reservoir computing is a bio-inspired approach especially suited for processing time-dependent information. The reservoir’s complex and high-dimensional transient response to the input signal is capable of universal computation. The reservoir does not need to be trained, which makes it very well suited for optics. As such, much of the promise of photonic reservoirs lies in their minimal hardware requirements, a tremendous advantage over other hardware-intensive neural net…
Robust finite-time fuzzy H∞ control for uncertain time-delay systems with stochastic jumps
2014
Abstract This paper investigates the problem of robust finite-time H ∞ control for a class of uncertain discrete-time Markovian jump nonlinear systems with time-delays represented by Takagi–Sugeno (T–S) model. Initially, the concepts of stochastic finite-time boundedness and stochastic finite-time H ∞ stabilization are presented. Then, by using stochastic Lyapunov–Krasovskii functional approach, sufficient conditions are derived such that the resulting close-loop system is stochastically finite-time bounded and satisfies a prescribed H ∞ disturbance attenuation level in a given finite-time interval. Furthermore, sufficient criteria on stochastic finite-time H ∞ stabilization using a fuzzy s…
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
2020
Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…
Visual information flow in Wilson-Cowan networks.
2020
In this paper, we study the communication efficiency of a psychophysically tuned cascade of Wilson-Cowan and divisive normalization layers that simulate the retina-V1 pathway. This is the first analysis of Wilson-Cowan networks in terms of multivariate total correlation. The parameters of the cortical model have been derived through the relation between the steady state of the Wilson-Cowan model and the divisive normalization model. The communication efficiency has been analyzed in two ways: First, we provide an analytical expression for the reduction of the total correlation among the responses of a V1-like population after the application of the Wilson-Cowan interaction. Second, we empiri…
Complete sequencing of Novosphingobium sp. PP1Y reveals a biotechnologically meaningful metabolic pattern.
2014
Background Novosphingobium sp. strain PP1Y is a marine α-proteobacterium adapted to grow at the water/fuel oil interface. It exploits the aromatic fraction of fuel oils as a carbon and energy source. PP1Y is able to grow on a wide range of mono-, poly- and heterocyclic aromatic hydrocarbons. Here, we report the complete functional annotation of the whole Novosphingobium genome. Results PP1Y genome analysis and its comparison with other Sphingomonadal genomes has yielded novel insights into the molecular basis of PP1Y’s phenotypic traits, such as its peculiar ability to encapsulate and degrade the aromatic fraction of fuel oils. In particular, we have identified and dissected several highly …
Resonance ionization schemes for high resolution and high efficiency studies of exotic nuclei at the CRIS experiment
2019
© 2019 This paper presents an overview of recent resonance ionization schemes used at the Collinear Resonance Ionization Spectroscopy (CRIS) setup located at ISOLDE, CERN. The developments needed to reach high spectral resolution and efficiency will be discussed. Besides laser ionization efficiency and high resolving power, experiments on rare isotopes also require low-background conditions. Ongoing developments that aim to deal with beam-related sources of background are presented. ispartof: Nuclear Instruments & Methods In Physics Research Section B-Beam Interactions With Materials And Atoms vol:463 pages:398-402 ispartof: location:SWITZERLAND, CERN, Geneva status: published
Artificial neural networks for neutron/ γ discrimination in the neutron detectors of NEDA
2020
Three different Artificial Neural Network architectures have been applied to perform neutron/? discrimination in NEDA based on waveform and time-of-flight information. Using the coincident ?-rays from AGATA, we have been able to measure and compare on real data the performances of the Artificial Neural Networks as classifiers. While the general performances are quite similar for the data set we used, differences, in particular related to the computing times, have been highlighted. One of the Artificial Neural Network architecture has also been found more robust to time misalignment of the waveforms. Such a feature is of great interest for online processing of waveforms. Narodowe Centrum Nau…
Optimising the Collinear Resonance Ionisation Spectroscopy (CRIS) experiment at CERN-ISOLDE
2020
© 2019 The CRIS experiment at CERN-ISOLDE is a dedicated laser spectroscopy setup for high-resolution hyperfine structure measurements of nuclear observables of exotic isotopes. Between 2015 and 2018 developments have been made to improve the background suppression, laser-atom overlap and automation of the beamline. Furthermore, a new ion source setup has been developed for offline studies. Here we present the latest technical developments and future perspectives for the experiment. ispartof: Nuclear Instruments & Methods In Physics Research Section B-Beam Interactions With Materials And Atoms vol:463 pages:384-389 ispartof: location:SWITZERLAND, CERN, Geneva status: published
Evolution of nuclear structure in neutron-rich odd-Zn isotopes and isomers
2017
Collinear laser spectroscopy was performed on Zn (Z=30) isotopes at ISOLDE, CERN. The study of hyperfine spectra of nuclei across the Zn isotopic chain, N=33–49, allowed the measurement of nuclear spins for the ground and isomeric states in odd-A neutron-rich nuclei up to N=50. Exactly one long-lived (&