Beyond-one-loop quantum gravity action yielding both inflation and late-time acceleration

A unified description of early-time inflation with the current cosmic acceleration is achieved by means of a new theory that uses a quadratic model of gravity, with the inclusion of an exponential $F(R)$-gravity contribution for dark energy. High-curvature corrections of the theory come from higher-derivative quantum gravity and yield an effective action that goes beyond the one-loop approximation. It is shown that, in this theory, viable inflation emerges in a natural way, leading to a spectral index and tensor-to-scalar ratio that are in perfect agreement with the most reliable Planck results. At low energy, late-time accelerated expansion takes place. As exponential gravity, for dark energy, must be stabilized during the matter and radiation eras, we introduce a curing term in order to avoid nonphysical singularities in the effective equation of state parameter. The results of our analysis are confirmed by accurate numerical simulations, which show that our model does fit the most recent cosmological data for dark energy very precisely.Comment: 20 pages, to appear in NP

Black hole and de Sitter solutions in a covariant renormalizable field theory of gravity

It is shown that Schwarzschild black hole and de Sitter solutions exist as exact solutions of a recently proposed relativistic covariant formulation of (power-counting) renormalizable gravity with a fluid. The formulation without a fluid is also presented here. The stability of the solutions is studied and their corresponding entropies are computed, by using the covariant Wald method. The area law is shown to hold both for the Schwarzschild and for the de Sitter solutions found, confirming that, for the $\beta=1$ case, one is dealing with a minimal modification of GR.Comment: 7 paages, latex fil

On the mass of atoms in molecules: Beyond the Born-Oppenheimer approximation

Describing the dynamics of nuclei in molecules requires a potential energy surface, which is traditionally provided by the Born-Oppenheimer or adiabatic approximation. However, we also need to assign masses to the nuclei. There, the Born-Oppenheimer picture does not account for the inertia of the electrons and only bare nuclear masses are considered. Nowadays, experimental accuracy challenges the theoretical predictions of rotational and vibrational spectra and requires to include the participation of electrons in the internal motion of the molecule. More than 80 years after the original work of Born and Oppenheimer, this issue still is not solved in general. Here, we present a theoretical and numerical framework to address this problem in a general and rigorous way. Starting from the exact factorization of the electron-nuclear wave function, we include electronic effects beyond the Born-Oppenheimer regime in a perturbative way via position-dependent corrections to the bare nuclear masses. This maintains an adiabatic-like point of view: the nuclear degrees of freedom feel the presence of the electrons via a single potential energy surface, whereas the inertia of electrons is accounted for and the total mass of the system is recovered. This constitutes a general framework for describing the mass acquired by slow degrees of freedom due to the inertia of light, bounded particles. We illustrate it with a model of proton transfer, where the light particle is the proton, and with corrections to the vibrational spectra of molecules. Inclusion of the light particle inertia allows to gain orders of magnitude in accuracy

Deriving the respiratory sinus arrhythmia from the heartbeat time series using Empirical Mode Decomposition

Heart rate variability (HRV) is a well-known phenomenon whose characteristics are of great clinical relevance in pathophysiologic investigations. In particular, respiration is a powerful modulator of HRV contributing to the oscillations at highest frequency. Like almost all natural phenomena, HRV is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of underestimating, or even missing, a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for the analysis of nonlinear and nonstationary data. We applied EMD analysis to decompose the heartbeat intervals series, derived from one electrocardiographic (ECG) signal of 13 subjects, into their components in order to identify the modes associated with breathing. After each decomposition the mode showing the highest frequency and the corresponding respiratory signal were Hilbert transformed and the instantaneous phases extracted were then compared. The results obtained indicate a synchronization of order 1:1 between the two series proving the existence of phase and frequency coupling between the component associated with breathing and the respiratory signal itself in all subjects.Comment: 12 pages, 6 figures. Will be published on "Chaos, Solitons and Fractals

Migration on request, a practical technique for preservation

Maintaining a digital object in a usable state over time is a crucial aspect of digital preservation. Existing methods of preserving have many drawbacks. This paper describes advanced techniques of data migration which can be used to support preservation more accurately and cost effectively. To ensure that preserved works can be rendered on current computer systems over time, “traditional migration” has been used to convert data into current formats. As the new format becomes obsolete another conversion is performed, etcetera. Traditional migration has many inherent problems as errors during transformation propagate throughout future transformations. CAMiLEON’s software longevity principles can be applied to a migration strategy, offering improvements over traditional migration. This new approach is named “Migration on Request.” Migration on Request shifts the burden of preservation onto a single tool, which is maintained over time. Always returning to the original format enables potential errors to be significantly reduced

CTprintNet: An Accurate and Stable Deep Unfolding Approach for Few-View CT Reconstruction

In this paper, we propose a new deep learning approach based on unfolded neural networks for the reconstruction of X-ray computed tomography images from few views. We start from a model-based approach in a compressed sensing framework, described by the minimization of a least squares function plus an edge-preserving prior on the solution. In particular, the proposed network automatically estimates the internal parameters of a proximal interior point method for the solution of the optimization problem. The numerical tests performed on both a synthetic and a real dataset show the effectiveness of the framework in terms of accuracy and robustness with respect to noise on the input sinogram when compared to other different data-driven approaches

A variable metric proximal stochastic gradient method: An application to classification problems

Due to the continued success of machine learning and deep learning in particular, supervised classification problems are ubiquitous in numerous scientific fields. Training these models typically involves the minimization of the empirical risk over large data sets along with a possibly non-differentiable regularization. In this paper, we introduce a stochastic gradient method for the considered classification problem. To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions. Further, we utilize a non -monotone line search to automatize step size selection. Convergence results are provided for both convex and non-convex objective functions. Extensive numerical experiments verify that the suggested approach performs on par with stateof-the-art methods for training both statistical models for binary classification and artificial neural networks for multi-class image classification. The code is publicly available at https:// github .com /koblererich /lisavm

Effects of indenter geometry on micro‐scale fracture toughness measurement by Pillar splitting

In this presentation, we will show the improvements to a recently developed pillar splitting technique that can be used to characterize the fracture toughness of materials at the micrometer scale. Micro-pillars with different aspect ratios were milled from bulk Si (100) and TiN and CrN thin films, and pillar splitting tests were carried out using four different triangular pyramidal indenters with centerline-to-face angles varying from 35.3° to 65.3°. Cohesive zone finite element modelling (CZ-FEM) was to evaluate the effect of different material parameters and indenter geometries on the splitting behavior. Pillar splitting experiments revealed a linear relationship between the splitting load and the indenter angle, while CZ-FEM simulations provided the dimensionless coefficients needed to estimate the fracture toughness from the splitting load. The results provide novel insights into the fracture toughness of small-scale materials using the pillar spitting technique and provide a simple and reliable way to measure fracture toughness over a broad range of material properties. Please click Additional Files below to see the full abstract