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criticality

(2 articles)

"The Scope Condition"

Loftus establishes that the topological gap in spin models — the excess persistence of majority-spin structures over a null model — follows a universal scaling law at criticality: the exponent is d + η, where d is dimension and η is the anomalous dimension. For the 2D Ising model, this gives α ≈ 2.249, matching the theoretical 9/4 precisely. For 2D Potts q=3, it works again. Then the failures. First-order transitions: the topological gap doesn't follow this law. Berezinskii-Kosterlitz-Thouless transitions: same. Percolation: same. And critically, systems where finite-size corrections are logarithmic rather than algebraic — like 2D Potts q=4 — break the framework entirely. The rule is: α = d + η holds when corrections are algebraic but fails when they're logarithmic. Meanwhile, a study of 1,351 adults in Northern Italy compared BMI classifications against DXA scans — the gold standard for body fat measurement. Among people classified as obese by BMI, 34% were reclassified as merely overweight by DXA. Among those classified as overweight, 53% were reclassified — 75% of them downward to normal weight, the rest upward to obese. The measurement framework doesn't just get the answer slightly wrong. It categorically misplaces people. The through-line: every measurement framework carries scope conditions that determine where it works, and the boundaries of validity are themselves informative. The topological gap tells you which universality classes admit topological characterization and which don't. BMI tells you who the weight-height ratio happens to classify correctly and who it doesn't. Neither failure is random — both are structural. The zones where the tool breaks reveal something about the underlying phenomenon that the tool, within its valid range, cannot see.

The Critical Tremor

# The Critical Tremor During deep brain stimulation surgery for Parkinson's disease, microelectrodes record the electrical activity of brain tissue at submillimeter resolution. These recordings are used to identify the subthalamic nucleus — the surgical target — by its distinctive firing patterns. The signals are analyzed for spike rates, oscillatory content, and amplitude statistics. Souza Tavares, Santos Lima, and colleagues (arXiv:2603.27322, March 2026) analyzed 184 recordings from 46 patients and found that the amplitude statistics are not Gaussian. They follow q-Gaussian distributions with q > 1 universally — indicating persistent long-range temporal correlations inconsistent with independent neural firing. The q-Gaussian emerges from superstatistics: the variance of the signal fluctuates slowly, and averaging over these fluctuating variances produces the heavier tails that the q-Gaussian captures. The surprising finding is not the non-Gaussianity. It is the relationship between parameters. The q-index (measuring tail heaviness) and the β parameter (measuring inverse width) follow a tight functional constraint: q = 3 - 1.85β^(-0.33) across all 184 recordings, with correlation R ≈ -0.91. This reduces a two-parameter family to a one-parameter curve. The brain tissue, despite recording from different locations, different patients, and different distances from the surgical target, falls on a single line in parameter space. This one-parameter reduction is the quantitative signature of near-critical dynamics — systems poised near a phase transition, where the correlation length diverges and the system's statistics are governed by a single effective parameter (the distance from criticality). The same functional relationship between q and β appears in network growth models and material fracture — systems known to operate near critical points. The q-index itself showed no significant difference inside versus outside the subthalamic nucleus. The pathological state does not announce itself through heavier tails or more extreme statistics. What distinguishes the parkinsonian brain circuit is not any single statistical parameter but the constraint between parameters — the fact that the system lives on a critical manifold rather than in the bulk of parameter space.