Oct 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
We present the Monte Carlo integration code MoCaNLO, which computes cross sections and distributions for processes at high-energy colliders like the LHC at leading and next-to-leading order (NLO) in the strong and electroweak couplings. It relies on the Recola package for the calculation of matrix elements and uses Catani–Seymour dipole subtraction for the treatment of infrared singularities. It has been used for several cutting-edge calculations of NLO QCD and electroweak corrections over the last years, such as NLO QCD corrections to off-shell top–antitop-quark production in association with a pair of bottom quarks and NLO electroweak corrections to vector-boson scattering processes.
This publication proposes a definition and a classification of agile software development approaches and analyses ten software development methods that can be characterized as being "agile" against the defined criterion.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 727 citations· ⚡54
The study shows that agile practices improve both informal and formal communication, but indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication.
M. Pikkarainen, Jukka Haikara, O. Salo et al.· Empirical Software Engineeri...· 401 citations· ⚡48
The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
The perception of the impact of agile methods is predominantly positive, and several challenge areas were discovered, but based on this study, agile methods are here to stay.
M. Laanti, O. Salo, P. Abrahamsson· Information and Software Tec...· 260 citations· ⚡20
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MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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