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 $\bar{q}q$ 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 $\bar{q}q$ states are below or above 1 GeV. We find the scalar states above 1 GeV to be preferred as $\bar{q}q$ 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