Skip to content
#edge computing Open access

§23.30 · Closing the Loop for Classes M and A: the Chain Is Free, and Why the Symmetric Crossing Is a Dead End

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Abstract§23.28 §7 flagged two related gaps for the vertex-figure classes M and A (hinge-sharing edge pairs whose tetrahedra do not share an edge): no discrete defect/holonomy loop had been defined for them, and no exponent formula existed for arbitrary defect multiplicities, since the §23.21 construction anchors both to a pair of tetrahedra T0a, T0b shared by e1 and e2 directly — which classes M and A lack by definition.A first attempt, reported here for completeness, searched for a genuinely new geometric object: a two-sided transport crossing between the two tetrahedra that e1and e2 DO each belong to (which share a face for class M, found in preliminary work on this note). Restricted to the unique transport thatfixes the hinge and exchanges the two bridge vertices — the only non-arbitrary choice — this crossing is well-defined and pair/hinge-independent, but it provably rotates axis(r′e2) onto axis(r′e1) exactly (cos γ 12′ = +1 to machine precision), collapsing the loop to a pure power of r′e1 and erasing the class-M information it was meant to capture (§2). The actual resolution needs no new object at all. Two facts, checked here across all 120 hinges with no exception, show that classes M and A already connect to the fully solved class-T machinery for free: (i) for a class-M pair, the two tetrahedra shared respectively by (e1 , ebridge) and by (ebridge, e2) already share a face — an ordinary wheel-step of the bridge edge’s own 5-cycle, not a new kind of step; (ii) for a class-A pair, the analogous 3-hop chain through 2 intermediate vertices is free in the same sense (§3). And the §23.21 §2.1–§2.2 exponent-transport identity, h·r′e1m·h−1 = ±(r′e2) m, holds for every valid conjugator h and every integer m — not just class T, but classes M and A too, unchanged (§4). Combining these: an ordinary §23.21-style bond defect on e1 gives k1 ; the SAME exponent-transportidentity that already built §23.21’s k2 formula for class T gives k2 for classes M and A too, with no modification; and Re(Lt) computed directly from the resulting quaternions matches the §23.28 closedform exactly. Verified on 1,056 cases spanning classes T, M and A, 4 hinges, and δ = 1..4, with maximum error 1.8×10−12 (§5). This closes both items of §23.28 §7: a discrete loop for M and A exists, built entirely from wheel-steps and the ordinary class-T crossing, chained through 1 or 2 intermediate vertices; and its defect-multiplicity exponents are exactly the §23.21 ones, since their derivation never used a shared tetrahedron between e1 and e2 in the first place — only that a conjugator h exists, which §23.28 §5(check D) already established for every class. Keywords600-cell · vertex figure · exponent transport conjugator h · wheel · discrete holonomy loop · classes M/A · exhaustive verification

View source

Similar papers

#computer vision Review Sep 2017

Agile Software Development Methods: Review and Analysis

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. · 727 citations · ⚡54
#computer vision Jun 2008

The impact of agile practices on communication in software development

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. · 401 citations · ⚡48
#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

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. · 394 citations · ⚡54

Related blog posts

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.