Inflation in asymptotically safe f(R) theory

We discuss the existence of inflationary solutions in a class of renormalization group improved polynomial f(R) theories, which have been studied recently in the context of the asymptotic safety scenario for quantum gravity. These theories seem to possess a nontrivial ultraviolet fixed point, where the dimensionful couplings scale according to their canonical dimensionality. Assuming that the cutoff is proportional to the Hubble parameter, we obtain modified Friedmann equations which admit both power law and exponential solutions. We establish that for sufficiently high order polynomial the solutions are reliable, in the sense that considering still higher order polynomials is very unlikely to change the solution.Comment: Presented at 14th Conference on Recent Developments in Gravity: NEB 14, Ioannina, Greece, 8-11 Jun 201

PyPore3D: An Open Source Software Tool for Imaging Data Processing and Analysis of Porous and Multiphase Media

n this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.)

Modelling fast forms of visual neural plasticity using a modified second-order motion energy model

The Adelson-Bergen motion energy sensor is well established as the leading model of low-level visual motion sensing in human vision. However, the standard model cannot predict adaptation effects in motion perception. A previous paper Pavan et al.(Journal of Vision 10:1-17, 2013) presented an extension to the model which uses a first-order RC gain-control circuit (leaky integrator) to implement adaptation effects which can span many seconds, and showed that the extended model's output is consistent with psychophysical data on the classic motion after-effect. Recent psychophysical research has reported adaptation over much shorter time periods, spanning just a few hundred milliseconds. The present paper further extends the sensor model to implement rapid adaptation, by adding a second-order RC circuit which causes the sensor to require a finite amount of time to react to a sudden change in stimulation. The output of the new sensor accounts accurately for psychophysical data on rapid forms of facilitation (rapid visual motion priming, rVMP) and suppression (rapid motion after-effect, rMAE). Changes in natural scene content occur over multiple time scales, and multi-stage leaky integrators of the kind proposed here offer a computational scheme for modelling adaptation over multiple time scales. © 2014 Springer Science+Business Media New York

Use of magnetic source imaging to assess recovery after severe traumatic brain injury—an MEG pilot study

RationaleSevere TBI (sTBI) is a devastating neurological injury that comprises a significant global trauma burden. Early comprehensive neurocritical care and rehabilitation improve outcomes for such patients, although better diagnostic and prognostic tools are necessary to guide personalized treatment plans.MethodsIn this study, we explored the feasibility of conducting resting state magnetoencephalography (MEG) in a case series of sTBI patients acutely after injury (~7 days), and then about 1.5 and 8 months after injury. Synthetic aperture magnetometry (SAM) was utilized to localize source power in the canonical frequency bands of delta, theta, alpha, beta, and gamma, as well as DC–80 Hz.ResultsAt the first scan, SAM source maps revealed zones of hypofunction, islands of preserved activity, and hemispheric asymmetry across bandwidths, with markedly reduced power on the side of injury for each patient. GCS scores improved at scan 2 and by scan 3 the patients were ambulatory. The SAM maps for scans 2 and 3 varied, with most patients showing increasing power over time, especially in gamma, but a continued reduction in power in damaged areas and hemispheric asymmetry and/or relative diminishment in power at the site of injury. At the group level for scan 1, there was a large excess of neural generators operating within the delta band relative to control participants, while the number of neural generators for beta and gamma were significantly reduced. At scan 2 there was increased beta power relative to controls. At scan 3 there was increased group-wise delta power in comparison to controls.ConclusionIn summary, this pilot study shows that MEG can be safely used to monitor and track the recovery of brain function in patients with severe TBI as well as to identify patient-specific regions of decreased or altered brain function. Such MEG maps of brain function may be used in the future to tailor patient-specific rehabilitation plans to target regions of altered spectral power with neurostimulation and other treatments

Material characterisation in phase contrast imaging: The basis decomposition method revisited

A method for basis decomposition of materials, based on hybrid phase-attenuation X-ray imaging, is presented. The effective composition of the imaged object in terms of two basis materials is reconstructed from X-ray images formed by coherent plane waves. The range of effectiveness of the technique is evaluated by means of a simplified model of image formation and a numerical simulation

Quantitative material characterization based on the spectral decomposition of X-ray tomographic images

A single-energy CT provides a map of gray levels simply related to X-rays linear attenuation coefficients, which could be very similar for different materials at a given energy. Images acquired at multiple energies allows for a quantitative description of an object. In this work, phantom images were acquired using synchrotron radiation CT at precisely defined energies. A successful attempt was made to differentiate the phantom materials with respect to their decomposition into basis materials

Geant4 implementation of inter-atomic interference effect in small-angle coherent X-ray scattering for materials of medical interest

An extension to Geant4 Monte Carlo code was developed to take into account inter-atomic (molecular) interference effects in X-ray coherent scattering. Based on our previous works, the developed code introduces a set of form factors including interference effects for a selected variety of amorphous materials useful for medical applications, namely various tissues and plastics used to build phantoms. The code is easily upgradable in order to include new materials and offers the possibility to model a generic tissue as a combination of a set of four basic components. A dedicated Geant4 application for the simulation of X-ray diffraction experiments was created to validate the proposed upgrade of Rayleigh scattering model. A preliminary validation of the code obtained through a comparison with EGS4 and an experiment is presented, showing a satisfactory agreement

Improvement in visual perception after high-frequency transcranial random noise stimulation (hf-tRNS) in those with migraine: an equivalent noise approach.

Migraine is a common neurological disorder with strong links to vision. Interictal migraine is thought to be characterised by internal noise in the brain, possibly due to increased variability in neural firing, which can be estimated using equivalent noise tasks. High-frequency transcranial random noise stimulation (hf-tRNS) can be used to modulate levels of internal noise in the brain, and so presents a possible therapy to redress noise levels in the migraine brain. This is a case-control study using a 2-alternative forced choice (2AFC) design. hf-tRNS and Sham control stimulation were used alongside a global motion direction discrimination task and visually based equivalent noise tasks. The migraine group demonstrated increased baseline internal noise levels compared to the control group. Internal noise levels, and sampling, were reduced using hf-tRNS but not Sham stimulation. However, there were no differences in terms of coherence thresholds, slopes, and lapse rate for global motion discrimination between the two groups. This is the first demonstration of the possibility of decreasing internal noise levels in migraine using hf-tRNS. Future work could explore the possibility of neurostimulation as a therapy for migraine