Data & Analytics

Framework for measuring causal emergence (ΦID) in active inference agents with perspective latents architecture, analyzing how architectural separation between fast perception and slow global latents affects information-theoretic signatures of integration. Use when studying causal emergence, active inference, or hierarchical agent architectures.

First XAI taxonomy for BCPNN mapping architectural primitives to 16 explanation primitives (P1-P16) and 5 design-time Configuration-as-Explanation primitives. Inherently transparent brain-like neural network with EU AI Act compliance.

Majorization lattice framework for proving entropy inequalities in classical and quantum information theory. Covers supermodularity and subadditivity of all sum-concave entropies (Shannon, Rényi, Tsallis) via structural majorization relations. Use when analyzing entropy inequalities, information-theoretic bounds, quantum state entropy comparisons, or proving subadditivity/supermodularity results.

应用信息论框架分析神经编码和神经群体动力学。包含互信息、信息瓶颈、传递熵等方法在神经科学中的应用。

Extracting interpretable higher-order topological features across multiple scales for Alzheimer's Disease classification using persistent homology. Captures connected components, cycles, and cavities from fMRI brain networks. Activation: higher-order topology, Alzheimer classification, persistent homology, brain network topology, topological features.

Hierarchical Bayesian Statistical Learning (HBSL) model for individual statistical learning trajectories from EEG data. Models how individuals discover structure in sensory sequences, with applications to dyslexia research and cognitive development. Activation: hierarchical Bayesian, statistical learning, EEG, individual differences, dyslexia, sequence structure, tone sequences.

Skill for implementing the differentiable Clone-Structured Causal Graph (gradCSCG) algorithm for end-to-end cognitive map learning from raw image sequences, as described in arXiv:2607.12382.

Systematic portfolio management methodology comparing classical to Bayesian portfolio construction approaches. Covers mean-variance optimization, Black-Litterman, Bayesian shrinkage, and hierarchical risk parity. Use when constructing portfolios, comparing portfolio optimization methods, implementing Bayesian portfolio techniques, or evaluating systematic investment strategies.

Bayesian decision-making framework for membership inference attacks on statistical releases using Bayesian network population models. Reframes membership inference with respect to populations represented as Bayesian networks, enabling more effective specialized attacks by incorporating prior information about attribute dependency structures. Use when analyzing statistical disclosure risk, designing membership inference attacks, or evaluating privacy of released statistics.

Bayesian dynamical framework for modeling time-order effects in sequential haptic perception. Captures perceptual biases from prior expectations and temporal structure using drift-diffusion dynamics. Activation: haptic perception, Bayesian dynamics, time-order effects, sequential stimuli, perceptual bias.

Attainable lower bounds for Bayesian quantum parameter estimation in qubit models, bridging classical Bayesian inference with quantum metrology limits (arXiv: 2607.07031)

Bayesian dynamic framework for modeling temporal order effects in tactile perception. Dynamic Bayesian modeling of perceptual discrimination tasks with temporal bias, prior-weighted sequential processing. Activation: tactile perception, temporal order effect, Bayesian inference, perceptual discrimination, dynamic model, somatosensory, temporal bias, sequential processing.

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

Recurrent Divisive Normalization Network (RDNN) framework for continuous working memory with low-rank slow manifolds. Provides implementation guidance for stable continuous manifold learning in RNNs using divisive normalization to prevent state space shattering into discretized point attractors. Use when modeling continuous working memory, neural manifolds, or stable RNN dynamics.

Quantum cloud platform authentication framework using multi-dimensional quantum fingerprints from raw measurement data. Constructs Mahalanobis-based fingerprints with drift early warning and adversarial detection to verify which physical device executes workloads, preventing hardware substitution attacks. Activation: quantum authentication, cloud verification, hardware fingerprinting, quantum cloud, device authentication, Mahalanobis distance, drift detection, adversarial detection, raw-curve

Random Riemann Zeta Function integral means spectrum methodology — connects random vertical shifts of zeta-function to Kraetzer's universal integral means spectrum conjecture via Gaussian multiplicative chaos (GMC). Use for: analytic number theory, random zeta functions, GMC, conformal mapping, multifractal analysis. arXiv: 2603.26507.

Zero-shot Quantum Neural Architecture Search methodology for VQA circuit optimization without classical search loop. Use when: (1) designing variational quantum circuits, (2) optimizing quantum architecture without expensive search, (3) reducing classical overhead in VQA, (4) NISQ-era algorithm design, (5) quantum machine learning circuit selection.

Beyond-symmetry structural design patterns for variational quantum machine learning. Use when designing VQML ansatze that go beyond symmetry constraints, selecting parametrizations that balance expressivity and trainability within symmetry-preserving subspaces, or analyzing structural choices in quantum neural network architectures. Covers equivariant VQA design, symmetry-breaking regularization, and structural ansatz selection criteria.

Variational Quantum Algorithms methodology covering CVQE (Cascaded Variational Quantum Eigensolver), certified QNN training via QIBP, and resource-efficient quantum optimization. Use when designing variational quantum circuits, optimizing NISQ-era algorithms, implementing certified quantum machine learning, or applying quantum algorithms to combinatorial optimization problems. Covers VQE variants, quantum interval bound propagation, compact binary encoding for quantum optimization, and divide...

Methodology for universal quantum computation using dissipatively stabilized multi-mode Schrödinger cat states via non-local dissipation engineering, based on arXiv:2607.13975.

Universal Neural Propagator (UNP) methodology for learning time evolution in many-body quantum systems. Transfers across both Hamiltonians and initial states simultaneously. Activation: neural propagator, quantum dynamics simulation, neural operator learning, quantum state evolution, UNP, universal propagator, quantum foundation model, neural quantum dynamics.

Pattern formation in multimode open quantum systems via GKSL master equation — extends Turing instabilities and mode competition to dissipative quantum systems with parametric driving and nonlinear damping.

Trustworthy Quantum Machine Learning roadmap covering reliability, robustness, and security in the NISQ era. Addresses QML-specific risks including probabilistic behavior, device noise, and hybrid pipeline vulnerabilities. Activation: trustworthy QML, quantum ML reliability, QML robustness, NISQ era quantum security, quantum ML safety.

IQP Quantum Circuit Born Machines trainability analysis under Gaussian initialization. Uses Stein's lemma and Lipschitz concentration bounds to derive analytical lower bounds on gradient variance and probabilistic concentration bounds for barren plateau avoidance in QCBMs. Activation: IQP circuit, Born machine, QCBM trainability, barren plateau, gradient concentration, Gaussian initialization, quantum generative model, MMD loss, Stein's lemma, Lipschitz bound, quantum machine learning