Towards Weyl fermions on the lattice without artefacts

In spite of the breakthrough in non-perturbative chiral gauge theories during the last decade, the present formulation has stubborn artefacts. Independently of the fermion representation one is confronted with unwanted CP violation and infinitely many undetermined weight factors. Renormalization group identifies the culprit. We demonstrate the procedure on Weyl fermions in a real representation

Nanowire electron scattering spectroscopy

Methods and devices for spectroscopic identification of molecules using nanoscale wires are disclosed. According to one of the methods, nanoscale wires are provided, electrons are injected into the nanoscale wire; and inelastic electron scattering is measured via excitation of low-lying vibrational energy levels of molecules bound to the nanoscale wire

Effect of wetting layers on the strain and electronic structure of InAs self-assembled quantum dots

The effect of wetting layers on the strain and electronic structure of InAs self-assembled quantum dots grown on GaAs is investigated with an atomistic valence-force-field model and an empirical tight-binding model. By comparing a dot with and without a wetting layer, we find that the inclusion of the wetting layer weakens the strain inside the dot by only 1% relative change, while it reduces the energy gap between a confined electron and hole level by as much as 10%. The small change in the strain distribution indicates that strain relaxes only little through the thin wetting layer. The large reduction of the energy gap is attributed to the increase of the confining-potential width rather than the change of the potential height. First-order perturbation calculations or, alternatively, the addition of an InAs disk below the quantum dot confirm this conclusion. The effect of the wetting layer on the wave function is qualitatively different for the weakly confined electron state and the strongly confined hole state. The electron wave function shifts from the buffer to the wetting layer, while the hole shifts from the dot to the wetting layer.Comment: 14 pages, 3 figures, and 3 table

Kinetics of exciton photoluminescence in type-II semiconductor superlattices

The exciton decay rate at a rough interface in type-II semiconductor superlattices is investigated. It is shown that the possibility of recombination of indirect excitons at a plane interface essentially affects kinetics of the exciton photoluminescence at a rough interface. This happens because of strong correlation between the exciton recombination at the plane interface and at the roughness. Expressions that relate the parameters of the luminescence kinetics with statistical characteristics of the rough interface are obtained. The mean height and length of roughnesses in GaAs/AlAs superlattices are estimated from the experimental data.Comment: 3 PostScript figure

Equal pay by gender and by nationality: a comparative analysis of Switzerland's unequal equal pay policy regimes across time

What explains the adoption of two different policies on equal pay by gender (EPG) and by nationality (EPN) in Switzerland? And why is the liberal, litigation-based, equal pay policy regime set up by the Gender Equality Act of 1996 much less effective than the neocorporatist ‘accompanying measures' to the Bilateral European Union-Switzerland Agreement on Free Movement of Persons adopted in 1999 to ensure equal pay for workers of different national origins? The formation of two different policy regimes cannot be explained by different levels of political will. Equally, different ‘varieties of capitalism' cannot explain the setup of the two different equal pay policy regimes within the very same country. Instead, our qualitative comparative analysis across time suggests that the differences can be best explained by a particular constellation of attributes, namely the use of different policy frames—i.e. ‘anti-discrimination' in the EPG and ‘unfair competition' in the EPN case—and the different setting of interest politics epitomised by the opposite stances adopted by Switzerland's employer associations in the two case

A prospective study on an innovative online forum for peer reviewing of surgical science

Background Peer review is important to the scientific process. However, the present system has been criticised and accused of bias, lack of transparency, failure to detect significant breakthrough and error. At the British Journal of Surgery (BJS), after surveying authors' and reviewers' opinions on peer review, we piloted an open online forum with the aim of improving the peer review process. Methods In December 2014, a web-based survey assessing attitudes towards open online review was sent to reviewers with a BJS account in Scholar One. From April to June 2015, authors were invited to allow their manuscripts to undergo online peer review in addition to the standard peer review process. The quality of each review was evaluated by editors and editorial assistants using a validated instrument based on a Likert scale. Results The survey was sent to 6635 reviewers. In all, 1454 (21.9%) responded. Support for online peer review was strong, with only 10% stating that they would not subject their manuscripts to online peer review. The most prevalent concern was about intellectual property, being highlighted in 118 of 284 comments (41.5%). Out of 265 eligible manuscripts, 110 were included in the online peer review trial. Around 7000 potential reviewers were invited to review each manuscript. In all, 44 of 110 manuscripts (40%) received 100 reviews from 59 reviewers, alongside 115 conventional reviews. The quality of the open forum reviews was lower than for conventional reviews (2.13 (± 0.75) versus 2.84 (± 0.71), P<0.001). Conclusion Open online peer review is feasible in this setting, but it attracts few reviews, of lower quality than conventional peer reviews

Three-Dimensional Ultrasound Guidance of Autonomous Robotic Breast Biopsy: Feasibility Study

Feasibility studies of autonomous robot biopsies in tissue have been conducted using real time 3D ultrasound combined with simple thresholding algorithms. The robot first autonomously processed 3D image volumes received from the ultrasound scanner to locate a metal rod target embedded in turkey breast tissue simulating a calcification, and in a separate experiment, the center of a water-filled void in the breast tissue simulating a cyst. In both experiments the robot then directed a needle to the desired target, with no user input required. Separate needle-touch experiments performed by the image-guided robot in a water tank yielded an rms error of 1.15 mm

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients