Symmetry breaking in commensurate graphene rotational stacking; a comparison of theory and experiment

Graphene stacked in a Bernal configuration (60 degrees relative rotations between sheets) differs electronically from isolated graphene due to the broken symmetry introduced by interlayer bonds forming between only one of the two graphene unit cell atoms. A variety of experiments have shown that non-Bernal rotations restore this broken symmetry; consequently, these stacking varieties have been the subject of intensive theoretical interest. Most theories predict substantial changes in the band structure ranging from the development of a Van Hove singularity and an angle dependent electron localization that causes the Fermi velocity to go to zero as the relative rotation angle between sheets goes to zero. In this work we show by direct measurement that non-Bernal rotations preserve the graphene symmetry with only a small perturbation due to weak effective interlayer coupling. We detect neither a Van Hove singularity nor any significant change in the Fermi velocity. These results suggest significant problems in our current theoretical understanding of the origins of the band structure of this material.Comment: 7 pages, 6 figures, submitted to PR

Silicon intercalation into the graphene-SiC interface

In this work we use LEEM, XPEEM and XPS to study how the excess Si at the graphene-vacuum interface reorders itself at high temperatures. We show that silicon deposited at room temperature onto multilayer graphene films grown on the SiC(000[`1]) rapidly diffuses to the graphene-SiC interface when heated to temperatures above 1020. In a sequence of depositions, we have been able to intercalate ~ 6 ML of Si into the graphene-SiC interface.Comment: 6 pages, 8 figures, submitted to PR

Mutation-aware fault prediction

We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 di↵erent predictive modelling techniques to 3 large real world systems (both open and closed source). The results show that our proposal can significantly (p 0.05) improve fault prediction performance. Moreover, mutation based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.http://www0.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/ISSTA2016-Bowesetal.pd

A wide band gap metal-semiconductor-metal nanostructure made entirely from graphene

A blueprint for producing scalable digital graphene electronics has remained elusive. Current methods to produce semiconducting-metallic graphene networks all suffer from either stringent lithographic demands that prevent reproducibility, process-induced disorder in the graphene, or scalability issues. Using angle resolved photoemission, we have discovered a unique one dimensional metallic-semiconducting-metallic junction made entirely from graphene, and produced without chemical functionalization or finite size patterning. The junction is produced by taking advantage of the inherent, atomically ordered, substrate-graphene interaction when it is grown on SiC, in this case when graphene is forced to grow over patterned SiC steps. This scalable bottomup approach allows us to produce a semiconducting graphene strip whose width is precisely defined within a few graphene lattice constants, a level of precision entirely outside modern lithographic limits. The architecture demonstrated in this work is so robust that variations in the average electronic band structure of thousands of these patterned ribbons have little variation over length scales tens of microns long. The semiconducting graphene has a topologically defined few nanometer wide region with an energy gap greater than 0.5 eV in an otherwise continuous metallic graphene sheet. This work demonstrates how the graphene-substrate interaction can be used as a powerful tool to scalably modify graphene's electronic structure and opens a new direction in graphene electronics research.Comment: 11 pages, 7 figure

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Development of a brief multidisciplinary education programme for patients with osteoarthritis

Background Osteoarthritis (OA) is a prevalent progressive musculoskeletal disorder, leading to pain and disability. Patient information and education are considered core elements in treatment guidelines for OA; however, there is to our knowledge no evidence-based recommendation on the best approach, content or length on educational programmes in OA. Objective: to develop a brief, patient oriented disease specific multidisciplinary education programme (MEP) to enhance self-management in patients with OA. Method Twelve persons (80% female mean age 59 years) diagnosed with hand, hip or knee OA participated in focus group interviews. In the first focus group, six participants were interviewed about their educational needs, attitudes and expectations for the MEP. The interviews were transcribed verbatim and thereafter condensed. Based on results from focus group interviews, current research evidence, clinical knowledge and patients' experience, a multidisciplinary OA team (dietist, nurse, occupational therapist, pharmacist, physical therapist and rheumatologist) and a patient representative developed a pilot-MEP after having attended a work-shop in health pedagogics. Finally, the pilot-MEP was evaluated by a second focus group consisting of four members from the first focus group and six other experienced patients, before final adjustments were made. Results The focus group interviews revealed four important themes: what is OA, treatment options, barriers and coping strategies in performing daily activities, and how to live with osteoarthritis. Identified gaps between patient expectations and experience with the pilot-programme were discussed and adapted into a final MEP. The final MEP was developed as a 3.5 hour educational programme provided in groups of 6-9 patients. All members from the multidisciplinary team are involved in the education programme, including a facilitator who during the provision of the programme ensures that the individual questions are addressed. As part of an ongoing process, a patient representative regularly attends the MEP and gives feedback concerning content and perceived value. Conclusion A MEP has been developed to enhance self-management in patients with OA attending a multidisciplinary OA outpatient clinic. The effectiveness of the MEP followed by individual consultations with members of the multidisciplinary team is currently evaluated in a randomised controlled trial with respect to patient satisfaction and functioning