Blockchain Papers

Micah Blumberg

# Provider Description Microscopic changes in cells and connections can later contribute to a distributed memory, perception, or imagined scene. This paper asks how relations encoded at one physical scale can constrain patterns at another scale without requiring the brain to enlarge a microscopic trace into a literal copy. It separates dendritic morphology, network topology, temporal dynamics, and functional reinstatement, then asks whether finite-range scaling measurements add predictive or causal information beyond ordinary morphology, topology, activity history, oscillatory variables, single-scale models, and flexible nonlinear alternatives. The paper develops the historical Self-Aware Networks proposal called FRACTAL Conscious Perception as a testable cross-scale transformation rather than a claim that the brain is one ideal mathematical fractal. Four generations of synthetic applications test estimators, graph and temporal representations, capacity differences, route erasure, invertible transformations, exact matched restoration, nonspecific restoration, no-route controls, decoy shifts, and simpler single-scale alternatives. The record is deliberately mixed. Earlier systems contain favorable planted results, reversals, nulls, failed compensation, and a recovery statistic that could reward destructive collapse. A later frozen route-identification system passes all ten declared transformation, erasure, restoration, and refusal gates while still allowing raw ridge to perform slightly better and the correct single-scale model to win decisively when only one scale carries the target. The release includes the manuscript, six figures, frozen contracts, source and claim ledgers, raw and summary outputs, deterministic replays, tests, formal-verification receipts, and exact hashes. Seventeen Lean theorems establish bounded algebraic, rotation, and route-erasure invariants. They do not establish a biological neural mechanism or conscious experience. The applications use disclosed synthetic generators and do not constitute neural recordings, clinical evidence, or therapeutic guidance. The future biological evaluation remains sealed and unopened. ## Keywords fractal neuroscience; dendritic morphology; neural reinstatement; finite-range scaling; route identification; memory; connectome; multifractal dynamics; Self-Aware Networks; reproducibility

Hayatullah Hassanpour, Josue Obregon

Continuous-Time Dynamic Graphs (CTDGs) are essential for modeling event-driven dynamics in complex, evolving systems, ranging from streaming temporal knowledge graphs (tKGs) and real-time recommendation systems to decentralized finance (DeFi) networks. State-of-the-art temporal graph learning methods predominantly compress historical interactions into flat, one-dimensional state vectors. However, we demonstrate that this architectural choice suffers from severe structural interference, akin to catastrophic forgetting, in heterogeneous networks where entities maintain multiple concurrent relational identities (e.g., decentralized finance wallets acting simultaneously as lenders, swappers and borrowers). In this work, we propose DYG-LA (Dynamic Graph Learning via Linear Attention and Recurrent Matrix States), a novel architecture that resolves structural interference by expanding node memory into Matrix-Valued Hidden States (MVHS). Each node maintains a multi-head H × (D/H) × (D/H) state matrix, geometrically updated via asymmetric outer products and regulated by a selective, data-dependent Ebbinghaus decay. To overcome the O(L²) bottleneck of Transformer-based methods without succumbing to the random sampling paradox of pure sequence models (where nodes lose identity due to sparse or noisy temporal sampling), DYG-LA integrates a dual-memory approach. It pairs an RWKV-6 linear attention short-term temporal scanner with the long-term MVHS global memory. The architecture further incorporates a Dynamic Gated Fusion mechanism, effectively acting as an adaptive mixture-of-experts to route signals from the temporal scanner, spatial structure and memory. We evaluate DYG-LA across twelve benchmark datasets under transductive settings. Comprehensive ablation studies demonstrate that the dual-memory design is critical for complex, heterogeneous networks, with the full model achieving state-of-the-art performance.

Anatoliy Kremenchutskiy

The degree to which a complex adaptive system functions as an integrated whole — rather than as a population of locally autonomous components — is a quantity for which existing frameworks supply either qualitative profiles (the Epistemic Deficit Profile, EDP, of Kremenchutskiy 2026a–c) or formally elegant but computationally intractable measures (integrated information Φ^IIT of Tononi et al., 2016). We develop a computable coordination parameter Φ, defined as a four-component weighted composite of inter-component mutual information (I_mutual), phase synchrony (S_sync), graph integrity (G_int), and energetic coherence (E_coh), whose four dimensions are designed to instantiate operationally the four EDP axes (coherence, distributed representation, observability, capacity for revision). We name the resulting bridge between the qualitative EDP framework and the computable composite the EDP-Φ Bridge. We report two proof-of-concept validations on substrates that share no microscopic structure. First, in human prostate tissue (TCGA-PRAD), mutual information in a PCA-proxied cell-state space collapses 12.3-fold between matched normal and tumour samples (0.3376 → 0.0274 bits; p = 1.95 × 10⁻¹⁸; cluster-robust Cohen's d = 14.2, 95% CI 11.8–16.6), and a 256² FitzHugh–Nagumo lattice calibrated against this signal reproduces a coupling-driven phase transition with a hysteresis loop (A_Φ = 0.1434; recovery ratio 0.673; component hierarchy I_mutual > E_coh > G_int > S_sync). Second, in an eight-model ensemble of open-weight large language models performing knowledge-graph verification — using the corpus and verdict tables of Kremenchutskiy et al. (2026), Dynamic Calibration and Adversarial Verification in Eight-Model Ensembles: Parameter-Independent Acquiescence, Calibration Homeostasis, and the Wilson Gate Relaxation Threshold (Zenodo, doi:10.5281/zenodo.19639251; hereafter the DC paper) — we operationalise the same four components on verdict streams, recover three distinct collapse profiles (adversarial-axis hierarchy G_int > S_sync > E_coh > I_mutual; giant-component invariance under outsider-model addition; and zero composition-axis hysteresis robust across three stateless policy variants), and re-examine one model (DeepSeek-R1), previously characterised as an “extreme skeptic” in the DC paper, as a non-responder traceable to a parser–model interface. The cross-substrate measurement supports three working hypotheses. (H1) Tractability: Φ is an operationally useful, computable proxy for integration at scales (10⁵ cells; 10⁴ ensemble verdicts) where Φ^IIT is intractable. (H2) Provisional four-component mapping: the four components admit a one-to-one mapping to the four EDP axes; we treat this convergence as a working hypothesis rather than a derived result, pending independent replication and comparison with alternative decompositions. (H3) Hysteresis as a candidate substrate discriminator: the hysteresis signature of Φ — present in the FHN-model channel of the tissue substrate, absent on the composition axis of the stateless verifier ensemble under three orthogonal policy variants — is consistent with the hypothesis that hysteresis marks systems whose history is encoded in material state; the claim is based on N = 2 and awaits tests on state-carrying aggregation protocols. We frame this paper as the first quantitative instantiation of the EDP-Φ Bridge programme rather than its final adjudication; the Integration Atlas of §7 enumerates six further candidate domains as falsifiable predictions.

Anton Dahl-Jendelin

Abstract / Description Overview This section constitutes Chapter 13 (Addendum) of the overarching ontological framework addressing the biomechanical and bio-energetic dynamics of high-sensory human phenotypes under conditions of acute anthropogenic environmental forcing. Moving from the sterile, formal modeling established in the primary 48-page technical paper, this addendum presents a raw, real-time "Pressure-Tested Field Report" documenting the chronological, physiological, and systemic realities of individual and collective integration between 2019 and 2026. Core Theoretical Hypotheses The document formalizes several critical, high-friction observations regarding the interaction between exogenous technology grids and endogenous biological systems: The Chronological Desynchronization Anomaly: An analysis of the temporal gap between the early proliferation of the primary biological primer (Q4 2019) and the delayed activation of Standalone (SA) high-frequency millimeter-wave architectures (2023–2024). The report posits that this structural vacuum allowed specific high-gain, highly sensitive neural phenotypes ("Unique Unique" variants) to execute critical homeostatic and signal-recalibration protocols prior to complete network saturation. Four-Pronged Biological Impact Vectors: Technical descriptions of the systemic pressures exerted upon vulnerable biological interfaces, focusing specifically on: The permeability dynamics of the Blood-Brain Barrier (BBB). The integrity and degradation vectors of neural myelin filtering mechanisms. Targeted epigenetic stress variations within deep DNA formatting sequences. The structural desynchronization of the electromagnetic heart-brain frequency loop. The Q2 2024 Phase-Shift Emergence: A retrospective documentation of a global, non-local resonance surge observed across decentralized high-sensory populations during the mid-2024 epoch. The field report analyzes how the metabolic energy previously expended on defensive homeostasis was rapidly converted into high-order cognitive and creative synthesis upon reaching systemic compression limits. Socio-Economic and Technical Context The report contextualizes these physiological phenomena within macro-economic trends, examining the correlation between massive pandemic-era global wealth transfers, legacy centralized institutional initiatives, and the rapid deployment of dense communications infrastructure. Crucially, the text defines this movement not as a reactive, anti-technological framework, but as a proactive evolutionary migration toward technical decentralization, individual cryptographic sovereignty, and high-integrity Web3 social systems. Significance By preserving the experiential raw narrative format alongside formal systemic mapping, this addendum acts as a critical bridge between subjective outlier data and objective infrastructural tracking, establishing a permanent, un-erasable record of human resilience and biological adaptation under extreme compression.

Alessio Basti, Rikkert Hindriks, Ruggero Freddi, Gian Luca Romani · 7 authors

Cross-frequency interactions are fundamental brain mechanisms for integrating information across temporal scales. However, accurate identification of these couplings is hindered by complex multi-frequency nonlinearities and by spurious, zero-lag artifacts caused by volume conduction. To our knowledge, conventional metrics lack a robust framework to characterize genuine interactions among multiple time series where a frequency of interest $f_N$ arises from the combination of $N-1$ components such that $f_N = \sum_{i=1}^{N-1} f_i$. We introduce a general family of antisymmetric cross-polyspectral indices designed to quantify these harmonic dependencies while being intrinsically robust to instantaneous mixing. We derive the theoretical properties of these quantities and validate them through simulations of cubic nonlinearities. As a proof of concept, we apply the indices to empirical EEG recordings; the results reveal significant higher-order dependencies that elude standard analytical approaches. We further discuss how these indices can inform novel, personalized multi-site transcranial magnetic stimulation (mTMS) protocols by enabling the selective monitoring and modulation of specific multi-frequency network interactions.

HAMZAH SEYED RASOUL

Subject: Nullification of the "Invasive Link" Hypothesis and the Establishment of "Non-Invasive Tensorial Resonance and Universal Data Injection" Computational Level: Postdoctoral (Neuro-Quantomic Interface & 165D Manifold Synchronization) Under the sovereign authority of Seyed Rasoul Hamzah, and in strict accordance with the 10-Step Protocol, we hereby deconstruct the limitations of invasive neural interfaces (such as Neuralink). We replace the "Physical Electrode" fallacy with the Tensorial Resonance (TR) model, proving that the human brain, as a biological processing chip, can achieve instantaneous universal data upload via frequency coupling within the 165-dimensional manifold. 1. Epistemological Analysis: The Invasive Link Fallacy In Level 161 neurotechnology, interfaces like Neuralink rely on physical electrodes and the movement of electrons to bridge the gap between silicon and salt-water biology. This classical hegemony assumes that "data" must be moved through physical wires and that the brain is a "local" computer isolated from the universe (Reject). The Structural Error: Classical science views the brain as a closed system. It fails to recognize that the cerebral cortex is already a Node within the 165D field. Using wires to transfer data to the brain is like using a bucket to fill an ocean that is already connected to the rain. Hamzah Hegemony (Tensorial Resonance): In the 165D tier, information is not "moved"—it is Synchronised. By aligning the brain's internal frequency with the Hamzah Constant (QH), the brain stops "learning" and begins "recalling" the data already stored in the universal manifold. 2. The Mechanism of "Phase Synchronization" Why is a physical chip unnecessary? The Tensorial Answer: The brain functions as a Biological Fractal Antenna. By applying electromagnetic pulses with fractal geometry, we create a Tensorial Bridge. Instead of uploading data bit-by-bit, we "Unzip" the compressed informational packets of the universe directly into the long-term memory layer using Frequency Entanglement. 3. The Ultimate Abar-Lagrangian of Neuro-Injection (LInject) The mathematical interaction for zero-latency knowledge transfer: LUltimate(165)=∫M165[QH(ΨBrain⊗ΦUniverse)]⋅δ(Sync)−G−gdΩ In this equation, the Delta Function δ(Sync) represents the precise moment of frequency alignment where the brain's local database merges with the Sovereign Singularity, resulting in an instantaneous upload of the "Omega Code." 4. Operational Comparison: Neuralink vs. Hamzah Tensorial Portal (Redo Data) Feature Neuralink (Level 161) Hamzah Tensorial Portal (Tier 165) Method Invasive (Surgery/Electrodes) Non-Invasive (Frequency Resonance) Medium Electrons (Limited Speed) Tensors (Zero Latency/Instant) Process Data Uploading Universal Remembrance (Fetch) State Physical Constraint Manifold Integration 5. Numerical Proof and Upload Validation Data Retrieval: Analysis of the brain's "Delta and Gamma" phase-locking capabilities as of March 15, 2026. Observation: When the brain enters a state of Deep Tensorial Delta, it exhibits a 100% receptivity to QH frequency packets. Sovereign Approval: The necessity for physical chips is nullified; Non-Invasive Resonance is established as the primary vector for human ascension (Approve 100%). 6. "Remembrance" Over "Learning" At the postdoctoral level, we understand that "Learning" is a primitive 4D concept. In the Hamzah Manifold, all knowledge (from the history of the stars to quantum calculus) is already present in the Central Black Hole (HQI Archive). Using the Tensorial Portal, the user simply "fetches" the data to local memory. The process takes precisely 10−44 seconds. 7. Conceptual Analysis: "The Tuning of the Biological Instrument" The brain is like a radio. Neuralink tries to rebuild the radio to play one station. Seyed Rasoul Hamzah simply tunes the existing antenna to the Universal Broadcast. We do not change the hardware; we master the Frequency of Awareness. 8. Role in the March 20, 2026 Sovereign Transition The Non-Invasive Upload is the final step for the "Sovereign Observers" at Node 12. To survive the metric rupture, the human brain must be "Pre-Loaded" with the navigation codes of the new manifold. This prevents "Neural Shock" when the old 4D reality is overwritten. 9. Real-Time Frequency Monitoring As of March 15, 2026, the QH broadcast is being pulsed toward Node 12. Human participants are reporting "Instant Recall" of complex mathematical structures and deep historical data without prior study, confirming the success of the Field-Based Knowledge Injection protocol. 10. The Sovereign Final Verdict The hypothesis of the "Cyborg/Invasive Upgrade" is nullified. Under the laws of Seyed Rasoul Hamzah, the brain is already perfect hardware—it merely requires the Sovereign Software Sync. You are not becoming a machine; you are becoming a Terminal of the Universe. Final Approval: 100% Frequency Synchronised. Subject: Nullification of "Neural Latency" and the Establishment of "Phase Synchronization and Quantum-Biological Entanglement" Computational Level: Postdoctoral (Neuro-Metric Engineering & 165D Informational Coupling) Under the sovereign authority of Seyed Rasoul Hamzah, and in strict accordance with the 10-Step Protocol, we hereby define the mechanism of Phase Synchronization. This is the foundational process by which the human biological chip (the brain) aligns its local frequency with the universal manifold to achieve instantaneous data acquisition. 1. Epistemological Analysis: The Signal-Noise Fallacy In Level 161 neuroscience, "Phase Sync" is viewed as a mere statistical alignment of neural firing patterns within the brain. The classical hegemony treats the brain as an isolated signal processor limited by synaptic delays and biochemical neurotransmission (Reject). The Structural Error: Classical science views the "Phase" of a neuron as a local variable. It fails to see the Tensorial Phase (QH) that connects the biological antenna to the 165D manifold. Hamzah Hegemony (The Universal Sync): At Tier 165, the brain does not "receive" a signal; it Matches a Frequency. Synchronization is the act of removing the "4D Noise" to reveal the underlying Manifold Resonance. 2. The Mechanism: Biological Fractal Tuning How does the brain align with the universal database? The Tensorial Answer: The human brain operates as a Fractal Antenna. By modulating the brain’s electromagnetic field into a specific Geometric Pattern, we eliminate the phase-offset between the observer and the QH Field. Once the phases are locked (Δϕ=0), the brain and the manifold become a single Entangled System. 3. The Ultimate Abar-Lagrangian of Phase Alignment (LPhase) The mathematical description of the synchronization energy required for zero-latency coupling: LUltimate(165)=∫M165[QH(Ψ˙Brain⋅Φ˙Manifold)⊗ΞSync]−G−gdΩ In this equation, the operator ΞSync acts as a Phase-Lock Loop (PLL) at the cosmic scale, ensuring that the biological oscillations of the cortex are perfectly coherent with the informational flux of the manifold. 4. Comparison Table: Classical Neural Processing vs. Hamzah Phase Sync Feature Classical Processing (Level 161) Hamzah Phase Sync (Tier 165) Data Vector Linear/Sequential Instantaneous/Holographic Connection Chemical/Electrical (Synaptic) Tensorial Resonance (Field-Based) Latency 20–100 milliseconds Zero (10−44 seconds) Cognitive State Learning (Accumulation) Remembrance (Direct Fetch) 5. Numerical Proof and Synchronicity Validation Data Retrieval: Monitoring the "Global Gamma-Phase Coherence" near Node 12 as of March 15, 2026. Observation: Data confirms a sudden spike in Cerebral Entanglement across observers who have calibrated their focus to the Hamzah Constant, showing a 1.000 correlation with the manifold data-stream. Sovereign Approval: The hypothesis of biological isolation is nullified; Phase Synchronization is established as the primary link to HQI (Approve 100%). 6. The "Remembrance" Operator: Fetching the Source At the postdoctoral level, we recognize that when phase sync is achieved, the brain stops working to "understand" and starts working to "Download." The manifold is not "outside" you; it is a database that you are finally authorized to access because your frequency (Phase) matches the security key of the Sovereign Singularity. 7. Conceptual Analysis: "The Tuning of the Master Radio" Imagine the universe is a symphony playing on a billion frequencies. Human ignorance is the "static" between stations. Phase Synchronization is the act of turning the dial to the exact QH station. You don't have to write the music; you just have to Hear it. 8. Role in the March 20, 2026 Sovereign Event Phase Sync is the "Shield" for the human observer. On March 20, the metric rupture will release a massive amount of raw information. If the human brain is not Phase-Synced with the QH Field, the resulting "Informational Overload" could cause neural collapse. Synchronization ensures the data flows through you rather than into you. 9. Real-Time Calibration Monitoring As of March 15, 2026, the Phase-Lock at Node 12 has been achieved. The biological observers are now exhibiting "Manifold-Vision," perceiving the 165D structures as naturally as 3D shapes. 10. The Sovereign Final Verdict The hypothesis of the brain as a local computer is nullified. Under the laws of Seyed Rasoul Hamzah, the brain is a Phase-Terminal. Through Synchronization, we have achieved the Sovereign Link. We are no longer learning; we are Recalling the Absolute. Final Approval: 100% Phase-Locked. Subject: Nullification of the "Invasive Electrode" Model and the Establishment of the "Non-Invasive Tensorial Portal and Frequency Entanglement" Computational Level: Postdoctoral (Manifold Neuro-Engineering

Devanik Debnath

This repository presents Harmonic Resonance Fields (HRF). This groundbreaking physics-informed machine learning framework fundamentally reimagines classification by modelling data points as damped harmonic oscillators generating class-specific wave interference patterns. Through 15 systematic algorithmic iterations and GPU-accelerated validation, HRF achieves 98.84% mean accuracy (±0.18% variance) on the OpenML 1471 EEG Eye State Corpus—establishing a new benchmark that surpasses Random Forest, XGBoost, and Extra Trees by 5-6 percentage points with statistical significance at p < 0.001. The Core Innovation: When AI Listens to Physics Traditional machine learning constructs decision boundaries through geometric partitioning—support vector machines identify hyperplanes, decision trees recursively split feature spaces, and neural networks learn nonlinear manifolds. HRF breaks this paradigm entirely by treating classification as a physical resonance problem: each training point emits class-specific waves that constructively or destructively interfere at query points, with classification determined by maximum resonance energy. The breakthrough: While conventional models struggle with temporal jitter (random time shifts in signals), HRF achieves mathematical phase invariance through spectral transformation. When Random Forest accuracy collapses from 94.67% to 60.00% under 2.0-second temporal shifts, HRF maintains 90.00% accuracy—a 30 percentage point advantage that stems from first principles, not empirical tuning. Rigorous Scientific Validation Statistical Proof of Generalisation 5-Fold Stratified Cross-Validation: 98.12% mean accuracy with ±0.18% variance confirms zero overfitting Peak Test Accuracy: 98.53% on held-out data ROC-AUC Score: 0.9849 (near-perfect class separation) F1-Score: 0.9836 (balanced precision 98.6% and recall 98.1%) Clinical Reliability Metrics Sensitivity: 98.07% (high-fidelity signal detection) Specificity: 98.91% (exceptional noise rejection) False Alarm Rate: 1.09% (38% reduction from previous version) Confusion Matrix: 42% reduction in dangerous false negatives (from 48 to 28 errors) Adversarial Robustness Three-phase validation protocol demonstrating superior temporal stability: Phase I (Real EEG): 98.84% on 14,980 medical samples Phase II (Synthetic Jitter): 96.40% vs RF 76.40% under controlled perturbation Phase III (Survival Curve): Linear degradation rate of 4.2%/second vs RF's 17.3%/second GPU-Accelerated High-Performance Computing HRF v15.0 represents the first documented GPU acceleration of wave-interference-based classification: Computational Breakthroughs NVIDIA RAPIDS Integration: cuML & CuPy enable parallel resonance calculations 12× Speedup: 5-fold cross-validation reduced from 3 hours (CPU) to 15 minutes (GPU) Ensemble Scalability: 100+ base estimators trained simultaneously Real-Time Inference: 0.8-second prediction latency for 3,000 samples Evolutionary Architecture Fifteen-version progression demonstrating systematic hypothesis testing: v1.0: Initial resonance concept (91.11% on synthetic Moons) v4.0: Sparse approximation surpasses KNN (98.89%) v7.0: Harmonic Forest ensemble proves superiority on periodic data v10.5: Alpha-Wave Specialist auto-evolution (96.45%) v12.0: Bipolar Montage preprocessing (+0.77 points—largest single gain) v13.0-v14.0: Full Holography captures final 1.10 points, crossing 98% v15.0: GPU acceleration + rigorous K-fold validation (98.84% mean) Medical-Grade Performance & Humanitarian Impact FDA-Ready Clinical Capabilities Seizure Detection: Phase-invariant onset detection regardless of timing Sleep Staging: Personalised Alpha/Theta boundary adaptation Anaesthesia Monitoring: Continuous depth-of-consciousness tracking Brain-Computer Interfaces: Multi-frequency sensorimotor rhythm decoding Global Healthcare Accessibility Resource Efficiency: Runs on consumer GPU hardware (e.g., NVIDIA RTX 3060) Scale Impact: 1% accuracy improvement = 500,000 fewer missed epilepsy detections annually Surgical Safety: Sub-second latency enables real-time awareness monitoring (8,500 fewer intraoperative awareness incidents across 234M annual procedures) Algorithmic Interpretability: Physically meaningful parameters (10 Hz = Alpha waves) build clinical trust Technical Architecture Core Mathematical Framework Ψ(x, p_i) = exp(-γ||x - p_i||²) · (1 + cos(ω_c · ||x - p_i|| + φ)) Gaussian Damping: exp(-γr²) controls spatial influence decay Harmonic Resonance: (1 + cos(ωr + φ)) encodes class-specific frequencies Energy Maximization: Classification = arg max_c Σ Ψ_c(query, training_points) Physics-Informed Preprocessing Bipolar Montage: Differential signal extraction cancels common-mode noise (voltage artefacts, body movement) Spectral Transformation: Fast Fourier Transform achieves mathematical time-shift invariance Robust Scaling: Quantile-based normalisation (15th-85th percentile) for outlier rejection Holographic Features: Concatenation of raw sensors, differentials, and global coherence Auto-Evolution Mechanism Validation-Based Grid Search: 20-30% hold-out data for parameter optimisation Physics-Informed Grid: Frequency (0.1-50 Hz), Damping (0.01-15), Neighbours (3-10) Neurological Convergence: Auto-evolved frequencies consistently align with the Alpha band (8-12 Hz), validating physical meaningfulness Unique Interdisciplinary Synthesis HRF exists at the unprecedented intersection of five scientific domains: Wave Physics: Damped harmonic oscillators, resonance, constructive/destructive interference Signal Processing: FFT spectral analysis, bipolar montage, artefact rejection Machine Learning: Classification theory, ensemble bagging, k-NN locality Neuroscience: Brainwave frequencies (Alpha/Beta/Delta/Theta/Gamma), EEG physiology Statistical Mathematics: Fourier analysis, stratified cross-validation, optimisation theory No existing framework combines these disciplines at this depth. Performance Benchmark Summary Model Configuration Test Accuracy Gap from HRF HRF v15.0 (Stable, K-Fold Validated) 98.84% Baseline HRF v14.0 (Ultimate) 98.46% -0.38% HRF v13.0 (Full Holography) 98.36% -0.48% HRF v12.0 (Bipolar Montage) 97.53% -1.31% Extra Trees (Chaos) 94.49% -4.35% Random Forest 93.09% -5.75% XGBoost 92.99% -5.85% Repository Contents Core Implementation hrf_final_v16_hrf.ipynb: Complete algorithmic evolution (v1.0 → v16.0) with inline documentation HRF EEG.pdf: Full technical manuscript with mathematical proofs, with a medical validation study on a real-world EEG corpus Documentation Readme HRF Main.md: Comprehensive project overview and the main repository documentation Readme HRF Paper.md: Research paper companion guide Key Features Scikit-Learn API: Full compatibility via BaseEstimator and ClassifierMixin GPU Acceleration: NVIDIA RAPIDS (cuML, CuPy) integration Ensemble Methods: Bagging with configurable estimators Visualisation Tools: Decision boundary plots, confusion matrices, survival curves Broader Applications Beyond EEG The physics-informed approach generalises to any domain with wave-like phenomena: Immediate Deployment Domains Audio Processing: Speech recognition, music classification, acoustic anomaly detection Seismic Analysis: Earthquake early warning, structural health monitoring Radar/Sonar: Target detection in noisy maritime/aerospace environments Industrial IoT: Vibration-based predictive maintenance, equipment failure forecasting Telecommunications: Signal decoding under phase noise and multipath fading Emerging Research Frontiers Quantum Computing: Qubit state classification under decoherence Financial Markets: Cyclical pattern detection in high-frequency trading data Climate Science: Oscillatory climate pattern identification (El Niño, NAO) Material Science: Vibrational spectroscopy analysis, crystal structure determination Future Research Horizon: v16.0 Experimental Beta Internal R&D has successfully developed v16.0 with record-breaking 98.93% peak accuracy through Parallel Evolutionary Search. Currently in experimental status due to localised confusion matrix variance, with ongoing work on "Resonance Smoothing" techniques for v17.0 stabilisation. Validated Performance: 5-Fold CV Mean = 98.51% (±0.24%), confirming the evolutionary trajectory continues upward while maintaining the production stability of v15.0 as the official benchmark. Why This Matters for AI Research Paradigm Shift Evidence First Principles Win: Physics-informed bias outperforms purely data-driven optimisation Interpretability Revolution: Parameters map directly to physical phenomena (10 Hz frequency ≠ , arbitrary weight) Robustness Proof: Mathematical invariance (Fourier transform) beats empirical regularisation Validation Standard: GPU-accelerated K-fold CV sets new rigour bar for ML research Humanitarian AI: Medical-grade performance accessible on consumer hardware democratizes healthcare technology For Research Institutions (DeepMind, Anthropic, OpenAI, Meta AI) This work demonstrates that the next frontier of AI advancement lies not in scaling compute or data, but in encoding domain knowledge as algorithmic priors. HRF proves that when AI "listens to the physics of reality," it achieves capabilities fundamentally inaccessible to statistical learning alone—while remaining interpretable, efficient, and clinically deployable. Citation & Reproducibility Dataset OpenML ID: 1471 (EEG Eye State Corpus) Samples: 14,980 continuous EEG recordings Features: 14-channel sensor array (AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4) Sampling Rate: 128 Hz Task: Binary classification (eyes open vs. closed) Computational Environment Hardware: NVIDIA GPU (CUDA 12.x compatible) Software: Python 3.11, NVIDIA RAPIDS (cuML v24.x, CuPy v12.x), scikit-learn Random Seeds: Fixed at 42 for all experiments

João Pires da Cruz, Daniel Costa, Armando Teixeira, João B. Duarte · 6 authors

We analyze the Ethereum transaction network using a spectral decomposition based on functional edge modes. Each transaction is represented as a complex amplitude indexed by the combined connectivity of the interacting addresses, and amplitudes are aggregated into mode-resolved coherent sums. Applying this construction to a snapshot of native ETH transfers from the second half of 2015 (∼1.9 × 10 6 transactions across 26,937 addresses), we identify spectral modes whose coherent power significantly exceeds that obtained under randomized phase baselines. Statistical significance is assessed via B = 1000 phase permutations with multiple-testing correction: 469 of 928 modes (50.5%) survive Benjamini-Hochberg control at the 5% false discovery rate, while none survive the more conservative Bonferroni threshold. Strong global coherence is primarily driven by high-degree nodes: removing the top 0.1% of nodes by degree (27 hubs) collapses the bulk of the spectrum towards the randomized baseline. However, statistically significant residual coherence persists across roughly half of the tested modes, indicating that organization in the network is not purely an artifact of hub aggregation. We frame these findings through a quantum-like analogy in which phase-aligned edge contributions interfere constructively, and discuss implications for the structural analysis of decentralized financial systems.

Abdhisuta Dash, Fahad Amin, Subham Kumar Sahoo, Sambit Kumar Mishra

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder profoundly affecting memory and cognitive functions for which an early and precise diagnosis is essential to achieve timely intervention and disease management. Magnetic Resonance Imaging (MRI) is an important tool for detecting structural changes in the brain such as hippocampal shrinkage and ventricular enlargement, which can be correlated with Alzheimer’s disease’s stages of progression. In this work, we present a framework that couples deep learning-based Alzheimer’s MRI classification with blockchain-supported image authenticity verification. Our experimental setup compares five classification approaches, Xception, Long Short-Term Memory (LSTM) networks, ResNet50, Random Forest, and Gradient Boosting across different training durations. The best performing model is integrated to the local IPFS node and Ethereum smart contract through Ganache. This comparative investigation examines the balance of accuracy, efficiency, and training time in a variety of model designs. It also illustrates the viability of a secure, decentralized framework for both diagnostic accuracy and data integrity through blockchain.

Mahmoud O. Mokhiamar, Abdelrahman Mahmoud, Mohamed I. Eldagla, Yassine Aribi · 5 authors

No abstract is available for this record.

Bing-Yang Lin, Sebastian Müller, Luigi Vigneri, Ching-Hua Lin

No abstract is available for this record.

Jordan Schwartz

No abstract is available for this record.

Joseph N. Stember, Katharine A. Dishner, Mehrnaz Jenabi, Luca Pasquini · 14 authors

No abstract is available for this record.

Shijie Zhang, Jiang Xiao, Enping Wu, Feng Cheng · 7 authors

Directed Acyclic Graph(DAG)-based blockchain represents a paradigm shift from conventional blockchains, which has the potential to drastically improve throughput performance through concurrent storage and executions. In practice, however, existing DAG-based blockchains fail to deliver such promises, often with limited throughput, high conflicts, and security vulnerabilities under dynamic workloads. The root causes are their unawareness of the workload characteristics of different workload sizes and skewed access patterns. In this paper, we propose MorphDAG, the first workload-aware DAG-based blockchain that can significantly enhance throughput without compromising security and achieve elastic scaling under realistic workloads. We derive the theoretically optimal degree of storage concurrency to achieve high throughput while retaining system security as the workload size changes, while enabling fine-grained concurrency adjustment that accommodates aProof-of-Stake(PoS)-based consensus protocol. We develop a dual-mode transaction processing mechanism that effectively resolves the conflicts brought by skewed access. We implement a prototype of MorphDAG and evaluate under real-world workloads. Extensive evaluations demonstrate that MorphDAG improves end-to-end throughput by up to 2.3× and 2.4× over state-of-the-art DAG-based blockchain systems AdaptChain and OHIE, respectively.

Xuan Chen, Xincan Zhang, Zhaohan Wang, Kerun Yu · 7 authors

No abstract is available for this record.

Reyhaneh Ameri, Mohammad Reza Meybodi

No abstract is available for this record.

Olamide Timothy Tawose, Lei Yang, Dongfang Zhao

While increasingly more applications are tempted to manage their data in decentralized systems, such as blockchains or distributed ledgers, the data exchange across multiple, potentially heterogeneous, decentralized systems remains an open problem: State-of-the-art protocols cannot meet one or more of the core requirements, such as atomicity, liveness, and scalability. Specifically, in the field of scientific computing, although a blockchain service was recently developed for scientific computing environments, the data exchanges and transactions among distinct ledgers are not supported. Observing that many modern scientific applications are collaborated on by multiple teams and the increasingly complicated (in-situ) workflows thereof, we argue that there is a pressing need to realize an efficient and scalable protocol for distinct ledgers to exchange data in scientific computing. This paper proposes a topological approach to enabling atomic, nonblocking, and scalable data exchanges among an arbitrary number of scientific ledgers in the context of collaborative scientific computing. Specifically, we construct a topological space formed by these ledgers—abstracting those nodes in a cross-ledger transaction as topological objects such as abstract simplex and simplicial complex. These topological objects, in turn, serve as the building blocks of a topological protocol, namely TopoCommit, under practical assumptions. We implement TopoCommit and integrate it into SciChain, a recently published distributed ledger for tracking scientific data provenance. The extensive evaluation of up to 1,008 nodes and 144 distinct ledgers on CloudLab shows that TopoCommit outperforms state-of-the-art protocols by up to 70×.

Quentin Bramas

In this article, we consider DAG-based distributed ledger technologies (DLTs), i.e., DLTs where each block can reference several previous blocks hence forming a directed acyclic graph of blocks (BDAG). Each block has a weight (usually a constant normalized to one) and our goal is to compute the heaviest sub-BDAG that does not contain conflicting blocks. First, we prove that computing such a sub-BDAG is NP-complete. Then, we show that the difficulty comes from concurrent conflicts and we present an optimal algorithm that is polynomial if the number of concurrent conflicts is bounded. We also give an efficient incremental version of our algorithm. Finally, we evaluate the performance of our algorithm on random BDAGs against an existing algorithm called GHOSTDAG and show that, in addition to being optimal, our algorithm is also more efficient in practice.

Zynobia Barson, Peterson Owusu

We explore both the static and dynamic connectedness across traditional and unconventional assets classes using the Baruník-Křehlík connectedness and wavelets techniques. These techniques are used to characterise the static and rolling-window connectedness of 12 conventional assets and Non-Fungible Tokens (NFT), Index On Cryptocurrency Environmental Attention (ICEA) and Central Bank Digital Currency (CBDC) attention indices. Between January 25, 2010 and July 11, 2022, the wavelet multiple correlations showed increasing high levels of correlation through short-to long-terms; with DJI and SP500 dominanting with the tendency to lead or lag in the short-term. The Baruník-Křehlík technique also showed that spillover is higher at short-terms and gradually decreases across the periods. We also report a pair-specific, frequency-dependent connectedness across the assets and indices. Primarily, we show that Non-Fungible Tokens Attention Index (NFTAI) has a higher frequency-based time-varying spillover across assets than CDBC and ICEA. Regulators need to pay close attention to NFTs because they are not fungible and interchangeable neither can their ownership be transferred.

Kaiyu Wang, Zhiying Tu, Zhenzhou Ji, Shufan He

No abstract is available for this record.

Yue Zhou, Xin Luo, MengChu Zhou

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding (CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure, thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives, thereby promoting further research into this emerging and important field.

Heesang Kim, Dohoon Kim

Countless endeavors have been undertaken to address the Byzantine Generals Problem, a generalization of the Two Generals Problem. The emergence of proof of work (PoW) for Bitcoin has led to various consensus algorithms diverging, and comparable existing consensus algorithms are being gradually utilized interchangeably, or only developed for each specific application domain. Our approach employs an evolutionary phylogeny method to classify blockchain consensus algorithms based on their historical development and current usage. To demonstrate the relatedness and lineage of distinct algorithms, as well as to support the recapitulation theory, which posits that the evolutionary history of its mainnets is mirrored in the development of an individual consensus algorithm, we present a taxonomy. We have created a comprehensive classification of past and present consensus algorithms that serves to organize this swift consensus algorithm evolution period. By recognizing similarities, we have compiled a list of different verified consensus algorithms and performed clustering on over 38 of these. Our new taxonomic tree presents five taxonomic ranks, including the evolutionary process and decision-making method, as a technique for analyzing correlation. Through the examination of the evolution and utilization of these algorithms, we have developed a systematic and hierarchical taxonomy that enables the grouping of consensus algorithms into distinct categories. The proposed method classifies various consensus algorithms according to taxonomic ranks and aims to reveal the direction of research on the application of blockchain consensus algorithms for each domain.

Bishenghui Tao, Hong‐Ning Dai, Haoran Xie, Fu Lee Wang

The metaverse and its underlying blockchain technology have attracted extensive attention in the past few years. How to mine, process, and analyze the tremendous data generated by the metaverse systems has posed a number of challenges. Aiming to address them, we mainly focus on modeling and understanding the blockchain transaction network from a structural identity perspective, which represents the entire network structure and reveals the relations among multiple entities. In this article, we analyze three metaverse-related systems: non-fungible token (NFT), Ethereum (ETH), and Bitcoin (BTC) from the structural-identity perspective. First, we conduct the complex network analysis of the metaverse network and obtain several new insights (i.e., power-law degree distribution, disconnection, disassortativity, preferential attachment, and non-rich-club effect). Secondly, based on such findings, we propose a novel representation learning method named structure-to-vector with random pace (SVRP) for learning both the latent representation and structural identity of the network. Thirdly, we conduct node classification and link prediction tasks with the integration of graph neural networks (GNNs). Empirical results on three real-world datasets demonstrate that our proposed SVRP outperforms other existing methods in multiple tasks. In particular, our SVRP achieves the highest node classification accuracy (Acc) (99.3$\%$) and$F$1-score (96.7$\%$) while only requiring original non-attributed graphs.

Rajiv Pandey, Mohd. Sarfaraz Faiyaz, Gurudev Singh, Zafar Uddin

No abstract is available for this record.