This paper proposes a next-generation edge measurement and control system that integrates physical sensors, virtual sensors, generative AI, high-precision simulation, stochastic resonance, dynamic reconfigurable hardware, legal compliance engines, distributed ledgers, and Fail-Legal control. By combining real and virtual data, the system compensates for sensor failure, noise, sampling limits, communication degradation, and regulatory changes in real time. Its core concept is to shift control from “measure → judge → act” to “predict → verify → legalize → control → prove,” enabling safer, legally compliant, evidence-driven operation through Quantum Thought Circuit OS ASI.
We present \textbf{ORCHID} (\textit{Orchestrated Reduction Consensus for Hash-based Integrity in Distributed Ledgers}), a novel bio-inspired consensus protocol that maps the neuroscientific \emph{binding problem} -- how the brain integrates distributed neural oscillations into a unified conscious percept -- onto the distributed systems \emph{consensus problem}, how blockchain nodes agree on a single ledger state under Byzantine faults. Grounded in the Penrose--Hameroff Orchestrated Objective Reduction (Orch~OR) hypothesis and the Kuramoto synchronisation model, ORCHID equips each node with a quantum-noisy phase oscillator; consensus is triggered when the network's order parameter $r(t)$ crosses a \emph{binding threshold} $θ_b$, mirroring the gamma-band binding event in conscious perception. ORCHID is further strengthened by a coherence-weighted Quantum Secret Sharing (QSS) layer, extending the survey framework of Weinberg to a concrete consensus application. Simulation results on Watts--Strogatz small-world networks ($n=10$--$150$) demonstrate: (i)~the Kuramoto order parameter reaches $r_{\max}=0.988$ under coupling $K=3.0$, well above the theoretical critical coupling $K_c \approx 1.41$; (ii)~a sharp QSS fidelity phase transition at coherence $c^*\approx 0.82$, confirming Theorem~2; (iii)100\% consensus rate at all tested Byzantine fractions (0\%--40\%), with median convergence under 4~s for $n=30$; and (iv)~ORCHID achieves $O(n{\cdot}k)$ message complexity, outperforming PBFT's $O(n^2)$ at $n\geq150$. These results establish ORCHID as a scalable, biologically plausible, and quantum-augmented consensus mechanism for post-quantum distributed ledgers.
We propose a novel approach to marked Hawkes kernel inference which we name the moment-based neural Hawkes estimation method. Hawkes processes are fully characterized by their first- and second-order statistics through a Fredholm integral equation of the second kind. Using recent advances in solving partial differential equations with physics-informed neural networks, we provide a numerical procedure to solve this integral equation in high dimension. Together with an adapted training pipeline, we give a generic set of hyperparameters that produces robust results across a wide range of kernel shapes. We conduct an extensive numerical validation on simulated data. We finally propose two applications of the method to the analysis of the microstructure of cryptocurrency markets. In a first application, we extract the influence of volume on the arrival rate of BTC-USD trades and in a second application we analyze the causality relationships and their directions amongst a universe of 15 cryptocurrency pairs in a centralized exchange.
In this paper I report the discovery of neurons which showed a neural correlate with ongoing fluctuations of Bitcoin and Ethereum prices at the time of the recording. I used the publicly available dataset of Neuropixel recordings by the Allen Institute to correlate the firing rate of single neurons with cryptocurrency price. Out of ~40.000 recorded single neurons, ~70% showed a significant correlation with Bitcoin or Ethereum prices. Even when using the conservative Bonferroni correction for multiple comparisons, ~35% of neurons showed a significant correlation, which is well above the expected false positive rate of 5%. These results were due to "nonsense correlations": when correlating two signals which both evolve slowly over time, the chances of finding a significant correlation between the two are much higher than when comparing signals which lack this property.
This study will investigate the predictability of a Chaotic time-series data using Reservoir computing (Echo State Network), Deep-Learning(LSTM) and Machine- Learning(Linear, Bayesian, ElasticNetCV , Random Forest, XGBoost Regression and a machine learning Neural Network) on the short (1-day out prediction), medium (5-day out prediction) and long-term (30-day out prediction) pricing of Bitcoin and Ethereum Using a range of machine learning tools, to perform feature selection by permutation importance to select technical indicators on the individual cryptocurrencies, to ensure the datasets are the best for predictions per cryptocurrency while reducing noise within the models. The predictability of these two chaotic time-series is then compared to evaluate the models to find the best fit model. The models are fine-tuned, with hyperparameters, design of the network within the LSTM and the reservoir size within the Echo State Network being adjusted to improve accuracy and speed. This research highlights the effect of the trends within the cryptocurrency and its effect on predictive models, these models will then be optimized with hyperparameter tuning, and be evaluated to compare the models across the two currencies. It is found that the datasets for each cryptocurrency are different, due to the different permutation importance, which does not affect the overall predictability of the models with the short and medium-term predictions having the same models being the top performers. This research confirms that the chaotic data although can have positive results for shortand medium-term prediction, for long-term prediction, technical analysis basedprediction is not sufficient.