Variable redundancy product coders

Variable redundancy error detection code

First principles investigation of transition-metal doped group-IV semiconductors: Rx{_x}Y1−x_{1-x} (R=Cr, Mn, Fe; Y=Si, Ge)

A number of transition-metal (TM) doped group-IV semiconductors, R$_{x}$Y$_{1-x}$ (R=Cr, Mn and Fe; Y=Si, Ge), have been studied by the first principles calculations. The obtained results show that antiferromagnetic (AFM) order is energetically more favored than ferromagnetic (FM) order in Cr-doped Ge and Si with $x$=0.03125 and 0.0625. In 6.25% Fe-doped Ge, FM interaction dominates in all range of the R-R distances while for Fe-doped Ge at 3.125% and Fe-doped Si at both concentrations of 3.125% and 6.25%, only in a short R-R range can the FM states exist. In the Mn-doped case, the RKKY-like mechanism seems to be suitable for the Ge host matrix, while for the Mn-doped Si, the short-range AFM interaction competes with the long-range FM interaction. The different origin of the magnetic orders in these diluted magnetic semiconductors (DMSs) makes the microscopic mechanism of the ferromagnetism in the DMSs more complex and attractive.Comment: 14 pages, 2 figures, 6 table

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

Concepts, Developments and Advanced Applications of the PAX Toolkit

The Physics Analysis eXpert (PAX) is an open source toolkit for high energy physics analysis. The C++ class collection provided by PAX is deployed in a number of analyses with complex event topologies at Tevatron and LHC. In this article, we summarize basic concepts and class structure of the PAX kernel. We report about the most recent developments of the kernel and introduce two new PAX accessories. The PaxFactory, that provides a class collection to facilitate event hypothesis evolution, and VisualPax, a Graphical User Interface for PAX objects

Impact of DM direct searches and the LHC analyses on branon phenomenology

Dark Matter direct detection experiments are able to exclude interesting parameter space regions of particle models which predict an important amount of thermal relics. We use recent data to constrain the branon model and to compute the region that is favored by CDMS measurements. Within this work, we also update present colliders constraints with new studies coming from the LHC. Despite the present low luminosity, it is remarkable that for heavy branons, CMS and ATLAS measurements are already more constraining than previous analyses performed with TEVATRON and LEP data.Comment: 17 pages, 2 figure

On strain hardening mechanism in gradient nanostructures

Experiments have shown that a gradient design, in which grain size spans over four orders of magnitude, can make strong nanomaterials ductile. The enhanced ductility is attributed to the considerable strain hardening capability obtained in the gradient metals. A non-uniform deformation on the lateral sample surface is also observed. This might inject geometrically necessary dislocations (GNDs) into the sample. However, no direct evidence has been provided. Therefore the issues remain: why can the gradient structure generate high strain hardening, and how does it reconcile the strength-ductility synergy of gradient nanostructures? Here for the first time we quantitatively investigate the strain hardening of a gradient interstitial-free steel by developing a dislocation density-based continuum plasticity model, in which the interaction of the component layers in the gradient structure is represented by incorporating GNDs and back stress. It is demonstrated that both the surface non-uniform deformation and the strain-hardening rate up-turn can be quantitatively well predicted. The results also show that the strain hardening rate of the gradient sample can reach as high as that of the coarse-grained counterpart. A strength-ductility map is then plotted, which clearly show that the gradient samples perform much more superior to their homogeneous counterparts in strength-ductility synergy. The predicted map has been verified by a series of experimental data. A detailed analysis on GNDs distribution and back stress evolution at the end further substantiates our view that the good strain hardening capability results from the generation of abundant GNDs by the surface non-uniform deformation into the nano-grained layers of the gradient sample. (C) 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Mott physics, sign structure, ground state wavefunction, and high-Tc superconductivity

In this article I give a pedagogical illustration of why the essential problem of high-Tc superconductivity in the cuprates is about how an antiferromagnetically ordered state can be turned into a short-range state by doping. I will start with half-filling where the antiferromagnetic ground state is accurately described by the Liang-Doucot-Anderson (LDA) wavefunction. Here the effect of the Fermi statistics becomes completely irrelevant due to the no double occupancy constraint. Upon doping, the statistical signs reemerge, albeit much reduced as compared to the original Fermi statistical signs. By precisely incorporating this altered statistical sign structure at finite doping, the LDA ground state can be recast into a short-range antiferromagnetic state. Superconducting phase coherence arises after the spin correlations become short-ranged, and the superconducting phase transition is controlled by spin excitations. I will stress that the pseudogap phenomenon naturally emerges as a crossover between the antiferromagnetic and superconducting phases. As a characteristic of non Fermi liquid, the mutual statistical interaction between the spin and charge degrees of freedom will reach a maximum in a high-temperature "strange metal phase" of the doped Mott insulator.Comment: 12 pages, 12 figure