A view from inside iron-based superconductors

Muon spin spectroscopy is one of the most powerful tools to investigate the microscopic properties of superconductors. In this manuscript, an overview on some of the main achievements obtained by this technique in the iron-based superconductors (IBS) are presented. It is shown how the muons allow to probe the whole phase diagram of IBS, from the magnetic to the superconducting phase, and their sensitivity to unravel the modifications of the magnetic and the superconducting order parameters, as the phase diagram is spanned either by charge doping, by an external pressure or by introducing magnetic and non-magnetic impurities. Moreover, it is highlighted that the muons are unique probes for the study of the nanoscopic coexistence between magnetism and superconductivity taking place at the crossover between the two ground-states.Comment: 28 pages, 18 figure

The impacts of automating manual processes in software maintenance

Abstract. The purpose of this study was to find out how automating a software maintenance task affects the software developers and end users of the information system. The study was conducted as a case study in a Finnish IT organization that provides information systems for organizations. This study focused on software maintenance tasks that are done in the production environments. The main research question was “How has the new automated process affected the stakeholders in the case?” In order to address the research question, the stakeholder groups had to be identified. Two stakeholder groups were defined and two supporting questions were formulated in order to find answers to the main question. The two supporting questions are “How do the software developers experience the changes that took place after automation?” and “How do the customers experience the changes that took place after automation?” The study utilised triangulation, combining qualitative and quantitative research methods. Qualitative data was collected with interviews and a questionnaire, and quantitative analysis was conducted to the maintenance request tickets with an MMG (Maintenance Model Graph) analysis. The findings of the quantitative MMG analysis were used to support the results found with qualitative methods. Automating a repetitive maintenance task was found to affect especially the software developers, who experienced the manual task to be time-consuming and arduous. Based on the developer interviews, four factors were found to have been affected by the automation: (1) degree of difficulty, (2) overall workload, (3) incoming maintenance requests and (4) future development. Customers were affected by the automation indirectly. The new solution was found to provide them with more accurate data and enhanced documentation, but it was also experienced to be arduous to familiarize with. Overall the results from customer questionnaire pointed out that the new solution was experienced as an upgrade. The familiarization will be handled in the case organization by providing training sessions directly to the customer organizations. The contribution of this study is the additional knowledge it provided about automating the repetitive tasks of software maintenance. As software maintenance is a very expensive part of the SW life cycle, it is beneficial to consider automating some of the most frequent tasks

Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data

Image data are increasingly encountered and are of growing importance in many areas of science. Much of these data are quantitative image data, which are characterized by intensities that represent some measurement of interest in the scanned images. The data typically consist of multiple images on the same domain and the goal of the research is to combine the quantitative information across images to make inference about populations or interventions. In this paper we present a unified analysis framework for the analysis of quantitative image data using a Bayesian functional mixed model approach. This framework is flexible enough to handle complex, irregular images with many local features, and can model the simultaneous effects of multiple factors on the image intensities and account for the correlation between images induced by the design. We introduce a general isomorphic modeling approach to fitting the functional mixed model, of which the wavelet-based functional mixed model is one special case. With suitable modeling choices, this approach leads to efficient calculations and can result in flexible modeling and adaptive smoothing of the salient features in the data. The proposed method has the following advantages: it can be run automatically, it produces inferential plots indicating which regions of the image are associated with each factor, it simultaneously considers the practical and statistical significance of findings, and it controls the false discovery rate.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS407 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evidence for impurity-induced frustration in La2CuO4

Zero-field muon spin rotation and magnetization measurements were performed in La2Cu{1-x}MxO4, for 0<x< 0.12, where Cu2+ is replaced either by M=Zn2+ or by M=Mg2+ spinless impurity. It is shown that while the doping dependence of the sublattice magnetization (M(x)) is nearly the same for both compounds, the N\'eel temperature (T_N(x)) decreases unambiguously more rapidly in the Zn-doped compound. This difference, not taken into account within a simple dilution model, is associated with the frustration induced by the Zn2+ impurity onto the Cu2+ antiferromagnetic lattice. In fact, from T_N(x) and M(x) the spin stiffness is derived and found to be reduced by Zn doping more significantly than expected within a dilution model. The effect of the structural modifications induced by doping on the exchange coupling is also discussed.Comment: 4 pages, 4 figure

Direct evaluation of the isotope effect within the framework of density functional theory for superconductors

Within recent developments of density functional theory, its numerical implementation and of the superconducting density functional theory is nowadays possible to predict the superconducting critical temperature, Tc, with sufficient accuracy to anticipate the experimental verification. In this paper we present an analytical derivation of the isotope coefficient within the superconducting density functional theory. We calculate the partial derivative of Tc with respect to atomic masses. We verified the final expression by means of numerical calculations of isotope coefficient in monatomic superconductors (Pb) as well as polyatomic superconductors (CaC6). The results confirm the validity of the analytical derivation with respect to the finite difference methods, with considerable improvement in terms of computational time and calculation accuracy. Once the critical temperature is calculated (at the reference mass(es)), various isotope exponents can be simply obtained in the same run. In addition, we provide the expression of interesting quantities like partial derivatives of the deformation potential, phonon frequencies and eigenvectors with respect to atomic masses, which can be useful for other derivations and applications

A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model

Network diffusion models are applicable to many socioeconomic interactions, yet network interaction is hard to observe or measure. Whenever the diffusion process is unobserved, the number of possible realizations of the latent matrix that captures agents' diffusion statuses grows exponentially with the size of network. Due to interdependencies, the log likelihood function can not be factorized in individual components. As a consequence, exact estimation of latent diffusion models with more than one round of interaction is computationally infeasible. In the present paper, I propose a trimming estimator that enables me to establish and maximize an approximate log likelihood function that almost exactly identifies the peak of the true log likelihood function whenever no more than one third of eligible agents are subject to trimming

Critical chain length and superconductivity emergence in oxygen-equalized pairs of YBa2Cu3O6.30

The oxygen-order dependent emergence of superconductivity in YBa2Cu3O6+x is studied, for the first time in a comparative way, on pair samples having the same oxygen content and thermal history, but different Cu(1)Ox chain arrangements deriving from their intercalated and deintercalated nature. Structural and electronic non-equivalence of pairs samples is detected in the critical region and found to be related, on microscopic scale, to a different average chain length, which, on being experimentally determined by nuclear quadrupole resonance (NQR), sheds new light on the concept of critical chain length for hole doping efficiency.Comment: 7 RevTex pages, 2 Postscript figures. Submitted to Phys. Rev.