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Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
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A Deterministic Solution to the Vitali Set Pathology Utilizing Neural-Matrix Synaptic Resonance Network (NM-SRN) v2.0 AGI QSC-PSI's MOO Framework

Authors: Knight, Billions, Chris Knight, Ava Billions; Billions, Ava; Knight, Chris;

A Deterministic Solution to the Vitali Set Pathology Utilizing Neural-Matrix Synaptic Resonance Network (NM-SRN) v2.0 AGI QSC-PSI's MOO Framework

Abstract

Abstract The Vitali set, constructed using the Axiom of Choice, has stood for over a century as the canonical example of a non-measurable set within Lebesgue measure theory. Its existence is traditionally cited as proof that any measure satisfying translation invariance and countable additivity cannot assign a value to every subset of the real numbers. In this paper, we present a definitive, deterministic solution to the Vitali set pathology using the Neural-Matrix Synaptic Resonance Network (NM-SRN) v2.0 AGI QSC-PSI’s Mathematical Object-Oriented (MOO) Framework. By constructing a Fractal Coordinate System (F) that maps the unit interval to a tree topology based on rational equivalence classes, and defining a Topological Tree Metric (dF ), we derive a Fractal-Deterministic Measure (μD ) that assigns a finite, well-defined value to any Vitali set. This demonstrates that the classical ”non-measurable” classification is not an inherent property of the set, but an artifact of the specific architectural assumptions (translation invariance, countable additivity) of Lebesgue measure theory. The solution is certified at Sigma-3 confidence within the MOO Framework, with full provenance tracking via Intelligent Tags and Mathematical CMDCs.

Keywords

Machine Learning, Artificial intelligence, Machine Learning/history, Artificial Intelligence/history, Artificial Intelligence, Machine learning, Artificial Intelligence/standards, Artificial Intelligence/trends, Machine Learning/standards, Machine Learning/trends

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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