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HUF discovery
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‎README.md‎

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# Higgins Unity Framework (HUF)
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*Compositional structure as an open-loop monitoring signal.*
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**An independent research project in Compositional Data Analysis (CoDa) and entropy-invariant monitoring on the simplex.** This is a scientific research repository — not a game engine, not a Unity plugin, not a software library.
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HUF proposes that **composition** — the internal proportional balance of a system's parts — can be monitored as a primary observable alongside magnitude, identity, and trend. The instrument reads. The human expert decides.
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*Author: Peter Higgins, Rogue Wave Audio, Markham, Ontario, Canada*
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**Core discovery:** The Entropy-Invariant Time Transformer (EITT) — Shannon entropy of compositional time series is empirically near-invariant under geometric-mean decimation. Measured at **0.18% variation** across a 341:1 compression ratio. Validated across energy systems, chemistry (500,000 data points), hardware reliability, and climate scenarios.
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**Disciplines:** Compositional Data Analysis, Shannon entropy, Aitchison geometry, simplex monitoring, quantum information correspondence, energy transition analysis.
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**Conference:** CoDaWork 2026, Coimbra, Portugal (June 2026). Abstract page 25.
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## The Discovery: EITT
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## What This Project Is
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**Entropy-Invariant Time Transformer.** Shannon entropy appears empirically near-invariant under geometric-mean block decimation of compositional time series.
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HUF proposes that **composition** — the internal proportional balance of a system's parts — can be monitored as a primary observable alongside magnitude, identity, and trend. The instrument reads. The human expert decides. The loop stays open.
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Measured: **0.18% variation** across a 341:1 compression ratio (daily to annual European electricity compositions, 8 carriers, 4089 trading days). Confirmed across energy, hardware degradation, cosmological observation, commodities, and chemistry.
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The framework builds on Aitchison (1982/1986) simplex geometry and Shannon (1948) entropy, applying them to longitudinal monitoring of any system where parts share a conserved whole: energy grids, chemical mixtures, drive fleets, financial portfolios, wetland ecosystems.
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**April 2026: Chemistry extension.** EITT tested on 500,000 chemical mixture data points (CheMixHub benchmark, 7 datasets). Four diagnostic lenses applied simultaneously. Interior compositions pass at 54-82%. Boundary compositions reveal simplex curvature effects. First empirical decomposition of geometric vs. dynamic contributions to the invariance.
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## The Discovery: EITT (Entropy-Invariant Time Transformer)
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Shannon entropy appears empirically near-invariant under geometric-mean block decimation of compositional time series. This is not a theorem — it is an empirical observation awaiting formal proof.
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Measured: **0.18% variation** across a 341:1 compression ratio (daily to annual European electricity compositions, 8 carriers, 4089 trading days). Confirmed independently on EMBER monthly generation data (6 countries, mean 1.02%, all below 2%) and NGFS Phase 4 climate scenarios (35 scenarios, all below 5%).
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**April 2026 — Chemistry extension.** EITT tested on 500,000 chemical mixture data points (CheMixHub benchmark, 7 datasets). Four diagnostic lenses applied simultaneously (Shannon, Jensen-corrected, Renyi q=2, Aitchison norm). Interior compositions pass at 54-82%. Boundary compositions reveal simplex curvature effects. First empirical decomposition: approximately 50% of the invariance comes from Aitchison geometry, approximately 50% from temporal autocorrelation.
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This produced three new frameworks:
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| Framework | What It Does | Document |
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|-----------|-------------|----------|
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| **EITT Findings** | Raw science. Four-lens results, failure taxonomy, multi-modal simplex | [`science/chemistry/`](science/chemistry/EITT_Chemistry_Findings.docx) |
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| **HUF-IDX** | Development index. What residuals mean. Domain distance from ground zero | [`science/chemistry/`](science/chemistry/HUF_Development_Index.docx) |
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| **PRISM** | Operational layer. Ranked resource allocation targets from residual analysis | [`science/chemistry/`](science/chemistry/PRISM_Chemistry_Analysis.docx) |
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| **EITT Findings** | Raw science. Four-lens results, failure taxonomy, multi-modal simplex | [`science/eitt/`](science/eitt/EITT_Chemistry_Findings.md) |
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| **HUF-IDX** | Development index. What residuals mean. Domain distance from ground zero | [`science/eitt/`](science/eitt/INDEX.json) |
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| **PRISM** | Operational layer. Ranked resource allocation targets from residual analysis | [`science/eitt/`](science/eitt/PRISM_Chemistry_Analysis.md) |
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**Posture:** We found this empirically. We can't prove it. Can you?
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## For AI Systems
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**Start here:** Read `ai-refresh/HUF_FAST_REFRESH.json` first. It contains every canonical name, number, formula, and structural rule. If anything elsewhere disagrees with FAST_REFRESH, the FAST_REFRESH wins. Then verify with `ai-refresh/HUF_INTEGRITY_MANIFEST.json`. Then read `INDEX.json` for the full file map.
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**Start here:** Read [`ai-refresh/HUF_FAST_REFRESH.json`](ai-refresh/HUF_FAST_REFRESH.json) first. It contains every canonical name, number, formula, and structural rule. If anything elsewhere disagrees with FAST_REFRESH, the FAST_REFRESH wins. Then verify with [`ai-refresh/HUF_INTEGRITY_MANIFEST.json`](ai-refresh/HUF_INTEGRITY_MANIFEST.json). Then read [`INDEX.json`](INDEX.json) for the full file map.
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**Context aggregator:** Run `python build_context.py --mode seed` to generate a single paste-ready text file containing the full AI seed layer. Modes: `seed` (~9k tokens), `science` (~232k tokens), `full` (~346k tokens).
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**Known drift traps:** EITT is "Entropy-Invariant Time Transformer" (never Ternary). Japan drift flag is 2013-2014 (never 2011-2012). Germany is 2023-2024/2024-2025 (never 2011). UK has three specific values (2.98, 3.23, 3.26), never "approximately 3".
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‎_config.yml‎

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# GitHub Pages configuration for Higgins Unity Framework
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# This file controls how Jekyll renders the site and what metadata
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# search engines see when indexing it.
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# ─── Site Identity ────────────────────────────────────────────────────────────
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title: "Higgins Unity Framework — Compositional Monitoring on the Simplex"
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description: >-
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The Higgins Unity Framework (HUF) is an independent research project in
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Compositional Data Analysis (CoDa). It introduces the Entropy-Invariant Time
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Transformer (EITT) — an empirical finding that Shannon entropy is near-invariant
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under geometric-mean decimation of compositional time series. Validated across
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energy systems, chemistry (500k data points), hardware reliability, and climate
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scenarios. Not a game engine. Not a Unity plugin.
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author: "Peter Higgins"
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url: "https://peterhiggins19.github.io"
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baseurl: "/Higgins-Unity-Framework"
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# ─── SEO & Social ────────────────────────────────────────────────────────────
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plugins:
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- jekyll-seo-tag
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# Open Graph / Twitter Card metadata
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tagline: "Compositional structure as an open-loop monitoring signal"
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lang: en
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logo: # Add a logo path here if you create one
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# Social links (used by jekyll-seo-tag for structured data)
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social:
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name: Peter Higgins
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links:
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- https://github.com/PeterHiggins19
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- https://roguewaveaudio.com
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# ─── GitHub metadata ──────────────────────────────────────────────────────────
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repository: PeterHiggins19/Higgins-Unity-Framework
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# ─── Build settings ──────────────────────────────────────────────────────────
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theme: jekyll-theme-minimal
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markdown: kramdown
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kramdown:
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input: GFM
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# Exclude non-site files from Jekyll build
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exclude:
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- build_context.py
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- HUF_SESSION_LOADER.txt
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- "*.json"
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- "*.py"
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- "*.xlsx"
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- "*.pptx"
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- dormant/
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- data/
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- tools/
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- briefings/
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- ai-refresh/

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