8,456 research outputs found
Creating temperature dependent Ni-MH battery models for low power mobile devices
In this paper the methodology and the results of creating temperature
dependent battery models for ambient intelligence applications is presented.
First the measurement technology and the model generation process is presented
in details, and then the characteristic features of the models are discussed.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
Scalar mesons in a linear sigma model with (axial-)vector mesons
The structure of the scalar mesons has been a subject of debate for many
decades. In this work we look for states among the physical
resonances using an extended Linear Sigma Model that contains scalar,
pseudoscalar, vector, and axial-vector mesons both in the non-strange and
strange sectors. We perform global fits of meson masses, decay widths and
amplitudes in order to ascertain whether the scalar states are below
or above 1 GeV. We find the scalar states above 1 GeV to be preferred as
states.Comment: 6 pages, 1 figure, To appear in the proceedings of the XII. Hadron
Physics Conference, Bento Goncalves, Brasil, April, 22 - 27, 201
Sequential Symbolic Regression with Genetic Programming
This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the
problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference to GP. More importantly, they show the potential of the proposed strategy: an effective way of applying geometric semantic operators to combine different (partial) solutions, avoiding the exponential growth problem arising from the use of these operators
Noble internal transport barriers and radial subdiffusion of toroidal magnetic lines
Single trajectories of magnetic line motion indicate the persistence of a
central protected plasma core, surrounded by a chaotic shell enclosed in a
double-sided transport barrier : the latter is identified as being composed of
two Cantori located on two successive "most-noble" numbers values of the
perturbed safety factor, and forming an internal transport barrier (ITB).
Magnetic lines which succeed to escape across this barrier begin to wander in a
wide chaotic sea extending up to a very robust barrier (as long as L<1) which
is identified mathematically as a robust KAM surface at the plasma edge. In
this case the motion is shown to be intermittent, with long stages of
pseudo-trapping in the chaotic shell, or of sticking around island remnants, as
expected for a continuous time random walk.Comment: TEX file, 84 pages including 32 color figures. Higher quality figures
can be seen on the PDF file at
http://membres.lycos.fr/fusionbfr/JHM/Tokamap/JSP.pd
Elastic alpha-scattering of 112Sn and 124Sn at astrophysically relevant energies
The cross sections for the elastic scattering reactions
{112,124}Sn(a,a){112,124}Sn at energies above and below the Coulomb barrier are
presented and compared to predictions for global alpha-nucleus potentials. The
high precision of the new data allows a study of the global alpha-nucleus
potentials at both the proton and neutron-rich sides of an isotopic chain. In
addition, local alpha-nucleus potentials have been extracted for both nuclei,
and used to reproduce elastic scattering data at higher energies. Predictions
from the capture cross section of the reaction 112Sn(a,g)116Te at
astrophysically relevant energies are presented and compared to experimental
data.Comment: 20 pages, 10 figures, accepted for publication in Phys. Rev.
MEMTI: optimizing on-chip non-volatile storage for visual multi-task inference at the edge
The combination of specialized hardware and embedded non-volatile memories (eNVM) holds promise for energy-efficient
DNN inference at the edge. However, integrating DNN hardware accelerators with eNVMs still presents several challenges. Multi-level
programming is desirable for achieving maximal storage density on chip, but the stochastic nature of eNVM writes makes them prone
to errors and further increases the write energy and latency. We present MEMTI, a memory architecture that leverages a multi-task
learning technique for maximal reuse of DNN parameters across multiple visual tasks. We show that by retraining and updating only
10% of all DNN parameters, we can achieve efficient model adaptation across a variety of visual inference tasks. The system
performance is evaluated by integrating the memory with the open-source NVIDIA Deep Learning Architecture (NVDLA)
Generalized Killing equations and Taub-NUT spinning space
The generalized Killing equations for the configuration space of spinning
particles (spinning space) are analysed. Simple solutions of the homogeneous
part of these equations are expressed in terms of Killing-Yano tensors. The
general results are applied to the case of the four-dimensional euclidean
Taub-NUT manifold.Comment: 10 pages, late
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