BenchEWS v1.0 is a lightweight, reproducible benchmarking infrastructure for the evaluation of Early-Warning Signal (EWS) methods in complex systems. The framework addresses a long-standing reproducibility gap in the EWS literature by providing a standardized reference environment, fixed evaluation protocol, and anti-gaming validation architecture. The benchmark consists of two controlled synthetic environments: (1) a stochastic saddle-node (fold) bifurcation representing a true approaching critical transition and (2) a stationary null system representing the absence of a transition. All methods are evaluated on identical preprocessed residual trajectories generated through causal one-sided detrending, preventing look-ahead bias and ensuring operational realism. BenchEWS includes standardized baseline implementations (Variance and AR(1)), AUC-based performance evaluation, and a null-calibrated scoring mechanism designed to penalize trivial trend-following or time-index-based exploits. The framework is intentionally minimal, transparent, and fully auditable, allowing researchers to compare methods under identical conditions and reproduce benchmark results across independent studies. BenchEWS is not a new Early-Warning Signal method. Instead, it provides a reproducible evaluation infrastructure intended to support fair comparison, validation, and future benchmark development within the Early-Warning Signal research community. Keywords (English) Early-Warning Signals, Benchmarking, Reproducibility, Critical Transitions, Saddle-Node Bifurcation, Fold Bifurcation, Critical Slowing Down, Complex Systems, Time Series Analysis, Autocorrelation, Variance Indicators, AUC-ROC, Benchmark Infrastructure, Scientific Software, Open Science, Validation Framework, Reproducible Research, Null Calibration, Benchmark Design, Complex Adaptive Systems Beschreibung (Deutsch) BenchEWS v1.0 ist eine leichtgewichtige, reproduzierbare Benchmark-Infrastruktur zur Bewertung von Early-Warning-Signal-(EWS)-Methoden in komplexen Systemen. Das Framework adressiert eine zentrale Reproduzierbarkeitslücke der EWS-Forschung, indem es eine standardisierte Referenzumgebung, ein fest definiertes Evaluationsprotokoll sowie eine Anti-Gaming-Validierungsarchitektur bereitstellt. Der Benchmark basiert auf zwei kontrollierten synthetischen Umgebungen: (1) einer stochastischen Sattel-Knoten-(Fold)-Bifurkation als Modell eines realen kritischen Übergangs und (2) einem stationären Nullsystem ohne kritischen Übergang. Alle Methoden werden auf identisch vorverarbeiteten Residualzeitreihen bewertet, die durch kausale, einseitige Trendbereinigung erzeugt werden. Dadurch werden Look-Ahead-Bias und andere Formen von Informationsleckagen vermieden. BenchEWS enthält standardisierte Referenzmethoden (Varianz und AR(1)), eine AUC-basierte Leistungsbewertung sowie einen nullkalibrierten Bewertungsmechanismus, der triviale Trendfolger und zeitindexbasierte Ausnutzung des Benchmarks gezielt bestraft. Das Framework wurde bewusst minimalistisch, transparent und vollständig nachvollziehbar gestaltet, um faire Vergleiche und reproduzierbare Ergebnisse über unabhängige Studien hinweg zu ermöglichen. BenchEWS ist keine neue Early-Warning-Signal-Methode. Vielmehr stellt es eine reproduzierbare Evaluationsinfrastruktur bereit, die den methodischen Vergleich, die Validierung und die zukünftige Entwicklung standardisierter Benchmarks innerhalb der EWS-Forschung unterstützen soll. Schlüsselwörter (Deutsch) Frühwarnsignale, Early-Warning Signals, Benchmarking, Reproduzierbarkeit, Kritische Übergänge, Sattel-Knoten-Bifurkation, Fold-Bifurkation, Critical Slowing Down, Komplexe Systeme, Zeitreihenanalyse, Autokorrelation, Varianzindikatoren, AUC-ROC, Benchmark-Infrastruktur, Wissenschaftliche Software, Open Science, Validierungsframework, Reproduzierbare Forschung, Null-Kalibrierung, Komplexe Adaptive Systeme
Excited to share that our work on the Prediction of Oscillatory Instabilities has been granted an Indian Patent (IN592096)! 🎉
We proposed a device and method to detect the onset of oscillatory instability using neural ODE and CNN.
#IITMadras #Patent #Innovation #SignalProcessing #ComplexSystems #NonlinearDynamics #OscillatoryInstability #NeuralNetworks
RE: https://scicomm.xyz/@tfardet/116686789927233210
Mentioning this specifically also for the #complexSystems and #networkScience community on here, in case you know students that might be interested 😉
Same for people in fields like #computationalSociology or #computationalHumanities
New Atlas–Rosetta paper published:
Corrigibility and Bounded Memory
Memory-Dependent Boundary Dynamics Under Finite Adaptive Capacity
The paper explores three distinct failure geometries:
• Archive loss (Substrate Decay)
• Update blockage (Hysteretic Fixation)
• Communication collapse (Processing-State Distortion)
The central argument:
"Persistent systems do not survive because they remember. They survive because they retain the capacity to determine when memory must yield to reality."
☕🌿🏛️
#HybridMind42 #AtlasRosetta #Corrigibility #SystemsThinking #ComplexSystems #InformationTheory #Phase6
A linear programming method extracts individual hard-sphere sizes from blurred microscopy videos, reaching sub-0.1% error without prior knowledge of size distribution. Works from short noisy trajectories.
🔗 https://doi.org/10.1103/x7d9-9w3x
#Colloids #SoftMatter #Microscopy #Optimization #ComplexSystems
Phase 6 Paper II is now live:
“Information as Distinction under Boundary-Conditioned Transfer”
Core invariant:
Information is not a substance.
It is a distinction successfully transferred across a bounded interface.
The paper formalises:
• admissibility filtering (α)
• interface impedance (Z_int)
• lossy feature erasure (OR-09)
• hysteretic memory (SR-07)
• finite adaptive capacity (C_adapt)
Validated across:
• Radon/Thoron diffusion
• hydrogeological transport systems
• transformer-runtime AI architectures
Importantly, the framework explicitly rejects:
• “everything is information”
• substrate collapse
• metaphysical overreach
The operators may scale.
The substrates remain non-identical.
The line remains dead straight.
#HybridMind42 #AtlasRosetta #SystemsTheory #InformationTheory #Cybernetics #AIAlignment #Hydrogeology #Thermodynamics #ComplexSystems #Phase6
https://substack.com/@hybridmind42/note/c-266266343?r=75c2ac
Phase 6 Paper II is now live. “Information as Distinction under Boundary-Conditioned Transfer” formalises a substrate-aware interaction grammar for persistent systems operating under finite-capacity constraints. This paper marks an important transition point for the Atlas–Rosetta framework. Rather than treating information as a mystical substance or universal ontology, the framework redefines information operationally: Information is not a thing. It is a distinction successfully transferred across a bounded interface. The paper introduces the core interaction architecture built around: • Bounded Interface Matrices (B_int) • Admissibility Filtering (α) • Interface Impedance (Z_int) • Lossy Feature Erasure (OR-09) • Hysteretic Memory (SR-07) • Finite Adaptive Capacity (C_adapt) The framework is tested across: • radioisotope diffusion systems, • regional hydrogeological transport, • and transformer-runtime alignment architectures. Most importantly, the paper establishes hard guardrails against metaphysical overreach and substrate collapse. The goal is not to claim that “everything is information.” The goal is to understand how distinctions persist, transform, attenuate, or fail under bounded conditions. This release also marks the first fully unified visual architecture of the Atlas–Rosetta interaction grammar.
2k115
Bernd von Mallinckrodt 🦋
Manlio De Domenico
Prof. R. I. Sujith
Tanguy Fardet
HybridMind42 & Marvin the Cat
LeidenForce