Correcting the polarization effect in low frequency Dielectric Spectroscopy

We demonstrate a simple and robust methodology for measuring and analyzing the polarization impedance appearing at interface between electrodes and ionic solutions, in the frequency range from 1 to $10^6$ Hz. The method assumes no particular behavior of the electrode polarization impedance and it only makes use of the fact that the polarization effect dies out with frequency. The method allows a direct and un-biased measurement of the polarization impedance, whose behavior with the applied voltages and ionic concentration is methodically investigated. Furthermore, based on the previous findings, we propose a protocol for correcting the polarization effect in low frequency Dielectric Spectroscopy measurements of colloids. This could potentially lead to the quantitative resolution of the $\alpha$-dispersion regime of live cells in suspension

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

Magnetic phase diagram of the Hubbard model

The competition between commensurate and incommensurate spin-density-wave phases in the infinite-dimensional single-band Hubbard model is examined with quantum Monte Carlo simulation and strong and weak coupling approximations. Quantum fluctuations modify the weak-coupling phase diagram by factors of order unity and produce remarkable agreement with the quantum Monte Carlo data, but strong-coupling theories (that map onto effective Falicov-Kimball models) display pathological behavior. The single-band model can be used to describe much of the experimental data in Cr and its dilute alloys with V and Mn.Comment: 12 pages plus 3 uuencoded postscript figures, ReVTe

Vacuum polarization for lukewarm black holes

We compute the renormalized expectation value of the square of a quantum scalar field on a Reissner-Nordström–de Sitter black hole in which the temperatures of the event and cosmological horizons are equal (“lukewarm” black hole). Our numerical calculations for a thermal state at the same temperature as the two horizons indicate that this renormalized expectation value is regular on both the event and cosmological horizons. We are able to show analytically, using an approximation for the field modes near the horizons, that this is indeed the case

Decay of accelerated particles

We study how the decay properties of particles are changed by acceleration. It is shown that under the influence of acceleration (1) the lifetime of particles is modified and (2) new processes (like the decay of the proton) become possible. This is illustrated by considering scalar models for the decay of muons, pions, and protons. We discuss the close conceptual relation between these processes and the Unruh effect.Comment: Latex2e, 12 pages, 6 Postscript figures included with epsfig, to appear in Phys. Rev.

NMR Evidence for Antiferromagnetic Transition in the Single-Component Molecular Conductor, [Au(tmdt)_{2}] at 110 K

We present the results of a ^{1}H NMR study of the single-component molecular conductor, [Au(tmdt)_{2}]. A steep increase in the NMR line width and a peak formation of the nuclear spin-lattice relaxation rate, 1/T_{1}, were observed at around 110 K. This behavior provides clear and microscopic evidences for a magnetic phase transition at considerably high temperature among organic conductors. The observed variation in 1/T_{1} with respect to temperature indicates the highly correlated nature of the metallic phase.Comment: 5pages, 6figures to be published in J. Phys. Soc. Jp

Enhancement of the electronic contribution to the low temperature specific heat of Fe/Cr magnetic multilayer

We measured the low temperature specific heat of a sputtered $(Fe_{23\AA}/Cr_{12\AA})_{33}$ magnetic multilayer, as well as separate $1000\AA$ thick Fe and Cr films. Magnetoresistance and magnetization measurements on the multilayer demonstrated antiparallel coupling between the Fe layers. Using microcalorimeters made in our group, we measured the specific heat for $4<T<30 K$ and in magnetic fields up to $8 T$ for the multilayer. The low temperature electronic specific heat coefficient of the multilayer in the temperature range $4<T<14 K$ is $\gamma_{ML}=8.4 mJ/K^{2}g-at$. This is significantly larger than that measured for the Fe or Cr films (5.4 and $3.5 mJ/K^{2}mol$ respectively). No magnetic field dependence of $\gamma_{ML}$ was observed up to $8 T$. These results can be explained by a softening of the phonon modes observed in the same data and the presence of an Fe-Cr alloy phase at the interfaces.Comment: 20 pages, 5 figure

Using neuroevolution for predicting mobile marketing conversion

This paper addresses user Conversion Rate (CVR) prediction within the context of Mobile Performance Marketing. Specifically, we adapt two main neuroevolution methods: Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT). First, we discuss two mechanisms for increasing execution speed (parallelism and data sampling); a strategy for preventing excessive network complexity with NEAT; and a rolling window scheme for performing an online learning. Then, we present experimental results, using distinct datasets and testing both offline and online learning environments.ThisarticleisaresultoftheprojectNORTE-01-0247-FEDER-017497,supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work was also supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019

Spin fluctuations in nearly magnetic metals from ab-initio dynamical spin susceptibility calculations:application to Pd and Cr95V5

We describe our theoretical formalism and computational scheme for making ab-initio calculations of the dynamic paramagnetic spin susceptibilities of metals and alloys at finite temperatures. Its basis is Time-Dependent Density Functional Theory within an electronic multiple scattering, imaginary time Green function formalism. Results receive a natural interpretation in terms of overdamped oscillator systems making them suitable for incorporation into spin fluctuation theories. For illustration we apply our method to the nearly ferromagnetic metal Pd and the nearly antiferromagnetic chromium alloy Cr95V5. We compare and contrast the spin dynamics of these two metals and in each case identify those fluctuations with relaxation times much longer than typical electronic `hopping times'Comment: 21 pages, 9 figures. To appear in Physical Review B (July 2000