Nonlocal correlations in iron pnictides and chalcogenides

Deviations of low-energy electronic structurse of iron-based superconductors from density-functional-theory predictions have been parametrized in terms of band- and orbital-dependent mass renormalizations and energy shifts. The former have typically been described in terms of a local self-energy within the framework of dynamical mean field theory, while the latter appears to require nonlocal effects due to interband scattering. By calculating the renormalized band structure in both random phase approximation (RPA) and the two-particle self-consistent approximation (TPSC), we show that correlations in pnictide systems like LaFeAsO and LiFeAs can be described rather well by a nonlocal self-energy. In particular, Fermi pocket shrinkage as seen in experiments occurs due to repulsive interband finite-energy scattering. For the canonical iron chalcogenide system FeSe in its bulk tetragonal phase, the situation is, however, more complex since even including momentum-dependent band renormalizations cannot explain experimental findings. We propose that the nearest-neighbor Coulomb interaction may play an important role in band-structure renormalization in FeSe. We further compare our evaluations of nonlocal quasiparticle scattering lifetime within RPA and TPSC with experimental data for LiFeAs

The state of peer-to-peer network simulators

Networking research often relies on simulation in order to test and evaluate new ideas. An important requirement of this process is that results must be reproducible so that other researchers can replicate, validate and extend existing work. We look at the landscape of simulators for research in peer-to-peer (P2P) networks by conducting a survey of a combined total of over 280 papers from before and after 2007 (the year of the last survey in this area), and comment on the large quantity of research using bespoke, closed-source simulators. We propose a set of criteria that P2P simulators should meet, and poll the P2P research community for their agreement. We aim to drive the community towards performing their experiments on simulators that allow for others to validate their results

New product innovations, information signalling and industry competition

This paper examines the impact of new product innovations on the market values of industry rivals. The evidence indicates that, on average, firms introducing new products experience a significantly positive valuation effect at announcement, while portfolios of industry rivals experience a significant negative valuation effect. This result is consistent with the hypothesis that signals of adverse changes in the competitive position of rivals dominate expected benefits from an innovation spillover. Crosssectional analysis of the announcement period returns reveals that the competitive effects are more pronounced in industries with less concentration and high leverage. Additionally, we find that industry rivals perform as well as the new product firms during the three years following the innovations. We conclude that over a longer period, rival firms are able to respond to the competitive disadvantage of the new product by some alternative innovation or an imitation.

Changes, Evolution and Bugs - Recommendation Systems for Issue Management

Changes in evolving software systems are often managed using an issue repository. This repository may contribute to information overload in an organization, but it may also help navigating the software system. Software developers spend much effort on issue triage, a task in which the mere number of issue reports becomes a significant challenge. One specific difficulty is to determine whether a newly submitted issue report is a duplicate of an issue previously reported, if it contains complementary information related to a known issue, or if the issue report addresses something that has not been observed before. However, the large number of issue reports may also be used to help a developer to navigate the software development project to find related software artifacts, required both to understand the issue itself, and to analyze the impact of a possible issue resolution. This chapter presents recommendation systems that use information in issue repositories to support these two challenges, by supporting either duplicate detection of issue reports or navigation of artifacts in evolving software systems