Data & Analytics

Framework for analyzing learning dynamics in low-rank RNNs via overlap space decomposition. Distinguishes loss-visible overlaps (determine activity/output/loss) from loss-invisible overlaps (encode training history). Enables understanding of why functionally equivalent networks learn differently. Activation: low-rank RNN learning, RNN overlap space, loss-visible invisible, RNN gradient descent dynamics, RNN learning theory, Ger Barak RNN.

Learning Dynamic Stability Landscapes in Synchronization Networks methodology - graph-to-image prediction paradigm for predicting stability landscapes from network topology. Pioneers image-like per-node stability landscapes beyond scalar indices. Applicable to neuroscience, power grids, biological synchronization. Activation: stability landscape, synchronization stability, graph-to-image prediction, dynamic stability, oscillator networks, power grid stability.

GTaS Generative Spike Train Model

Excitation-driven data generation and distributed control optimization for building thermal systems and district heating networks. Combines BuilDyn framework (arXiv:2605.29849) and distributed NMPC with ADMM (arXiv:2605.29841).

Theoretical analysis of effective target shift in online learning and methods to correct for it. Explains why online learning struggles under distributional shift and how to characterize the relationship between online and offline learning. Activation triggers: online learning, target shift, distributional shift, online vs offline learning, sequential learning theory

Methodology for auditing and distilling deep reinforcement learning hedging policies into interpretable symbolic formulas. Includes framework for analyzing delta corrections relative to Black-Scholes, symbolic regression distillation, and regime fragility stress-testing. Use when analyzing neural hedging strategies, quantitative risk management, options hedging with RL, or making black-box financial AI auditable.

Convex Hybrid Modeling methodology using operator theory for process control and systems engineering. Formulates convex learning problems that combine model interpretability with system identification efficiency. Covers three settings: (1) regularization around a reference model, (2) restriction on interpretable subspaces, (3) kernel-based mixture models on interpretable manifolds. Use when: building interpretable control models, combining physics-based and data-driven modeling, designing hyb...

Two-site cavity method for analyzing large nonlinear recurrent neural networks. Derives linear equivalence of nonlinear RNNs, computes full covariance matrices for specific quenched realizations, and separates Gaussian from non-Gaussian contributions in recurrent network dynamics. Use when analyzing: (1) high-dimensional RNN covariance structure, (2) nonlinear-to-linear network equivalence, (3) cavity method applications to neural dynamics, (4) quenched disorder in recurrent networks.

Two-stage interpretation of attention as in-context empirical Bayes inference via particle dynamics with posterior mean recovery guarantees

Artificial Intelligence applications in complex network science - network analysis, topology learning, dynamics prediction, and emergent behavior detection. Comprehensive survey covering AI potential, methodology, and applications. Use when analyzing complex networks, network topology learning, dynamics prediction, emergent behavior, social networks, biological networks, or transportation networks. Keywords: complex networks, network science, AI networks, topology dynamics, emergent behavior,...

Artificial Intelligence applications in complex network science - network analysis, topology learning, dynamics prediction, and emergent behavior detection. Comprehensive survey covering AI potential, methodology, and applications. Use when analyzing complex networks, network topology learning, dynamics prediction, emergent behavior, social networks, biological networks, or transportation networks. Keywords: complex networks, network science, AI networks, topology dynamics, emergent behavior,...

Comprehensive stock technical analysis system for fetching data, calculating indicators (KDJ, MACD, RSI, BOLL), generating visualizations and reports. Use when user asks about stock analysis, 股票分析, technical analysis, 技术分析, k-line, or stock scoring.

Theory of phase-locked activity in delayed spiking networks using the Haken Lighthouse model — an analytically tractable event-based framework bridging integrate-and-fire networks and coupled phase oscillators. Derives self-consistency conditions for phase-locked states with multiple fixed delays, linear stability theory formulated in spike-time perturbations, and activity-dependent white matter plasticity (myelination-modulated conduction speed) creating slow-fast state-dependent delay syste...

Epidemic spreading with activity predicts Alzheimer's.

Proposes that active sensing (energy expenditure for information) is not driven by sensory goals but is necessary for task-level control. Integrates empirical data and control theory to explain explore-exploit mode switching in biological sensorimotor systems. Use when researching active sensing, sensorimotor control, control theory in neuroscience, explore-exploit tradeoffs, or bio-inspired robotics.

C++ documentation style conventions for Coot codebase

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pa

Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

Interactive visualization library. Use when you need hover info, zoom, pan, or web-embeddable charts. Best for dashboards, exploratory analysis, and presentations. For static publication figures use m

Spreadsheet creation, format conversion (ODS/XLSX/CSV), formulas, data automation with LibreOffice Calc.

Master Julia 1.10+ with modern features, performance optimization, multiple dispatch, and production-ready practices.

Expert data scientist for advanced analytics, machine learning, and statistical modeling. Handles complex data analysis, predictive modeling, and business intelligence.

Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.