Sep 2026· Zenodo (CERN European Organization for Nuclear Research)· 1 references
Abstract
quantum-group is a small Python package that builds the explicit matrices behind concrete quantum-group calculations with exact SymPy arithmetic and checks each identity as an exact residual matrix. A nonzero residual entry locates the convention that disagrees: basis order, tensor placement, coproduct or, for superalgebras, the Koszul sign rule. Covered: finite-dimensional Uq(sl2) modules and their defining relations; coproduct, counit and antipode with generator-level Hopf identities; tensor products and Clebsch–Gordan highest-weight vectors; the fundamental R-matrix with quantum Yang–Baxter, braid, Hecke and coproduct-intertwining checks, in the historical and the coproduct-compatible convention; and the graded Yang–Baxter equation for the 9×9 GLq(2|1) R-matrix of Çelik and Çelik (2021), including Rij placements on tensor powers via graded swaps. Scope: the checks are exact verifications on explicit finite-dimensional representations, not proofs in the abstract algebra, and the package is not a general quantum-group computer algebra system. This release (1.1.1) has the same package code and test suite as 1.1.0 (10.5281/zenodo.23015576). It adds the userguide prepared for SciPost Physics Codebases (scipost/; the same text as an amsart preprint in arxiv/), its worked examples with stored expected outputs, and a cross-check against data exported from GAP's QuaGroup package; continuous integration runs every example and compares its output. Records of earlier journal submissions, the thesis poster build and other material not needed for the software were removed from the repository; they remain in its Git history. Install: pip install quantum-group==1.1.1. Validate: python -m pytest (202 tests and doctests). Requires Python ≥ 3.10, SymPy ≥ 1.10, NetworkX ≥ 2.6, Matplotlib ≥ 3.5. Source, documentation and issues: github.com/TerekliTahaBerk/quantum-groups.
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 results show that the embedded industry has been able to apply agile methods in its development processes and that the appreciation of the agile methods and their individual practices appears to increase once adopted and applied in practice.
O. Salo, P. Abrahamsson· IET Software· 238 citations· ⚡9
Consequences of happiness and unhappiness that are beneficial and detrimental for developers' mental well-being, the software development process, and the produced artifacts are found.
D. Graziotin, Fabian Fagerholm, Xiaofeng Wang et al.· Journal of Systems and Softw...· 236 citations· ⚡13
The Mobile-D approach is briefly outlined here and the experiences gained from four case studies are discussed, which helped develop an agile development approach for mobile application development.
P. Abrahamsson, Antti Hanhineva, H. Hulkko et al.· Conference on Object-Oriente...· 225 citations· ⚡18
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.