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SkillsMP has collected 4,114 skills from hiyenwong/ai_collection. Open a skill to review its source and details.
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Subject-specific analysis of self-initiated attention shifts from EEG with controlled internal and external attention conditions. Machine learning + SHAP feature attribution reveals that higher-frequency bands and frontal regions carry subject-specific…
Stochastic Physical Neural Networks (PNNs) methodology using single-electron and single-photon stochastic neurons. Training via empirical backward pass with few trials achieves >97% MNIST accuracy. Use when: physical neural networks, stochastic neurons,…
时序注意力增强变分图循环神经网络（TAVRNN）用于神经动力学和行为建模。整合概率图学习与时序注意力机制，建模时变神经连接。支持单单元级别潜在动力学和群体级别可解释表示。触发词：神经动力学、时变连接、图神经网络、TAVRNN、神经群体、行为解码、neuronal dynamics、time-varying connectivity、graph neural network、temporal attention。
Source text: Chinese
Virtual distillation framework extended to bosonic quantum systems using passive linear-optical interferometers for error-mitigated measurements in continuous-variable quantum computing.
Relaxing Warped Spaces — generalized hierarchical and modular dynamical neural networks. Uses warped hierarchical modular structure for efficient representation learning and dynamical neural processing. Applicable to neuromorphic computing, hierarchical…
NEURRATOR methodology for generating natural language descriptions of visual scenes from single-neuron spike trains. Uses CLIP embeddings and multimodal LLM for zero-shot decoding without language-side training.
Source text: Chinese
NEURRATOR - Semantic narration of vision at single-cell resolution. Maps spiking activity to natural-language descriptions via CLIP-LLaVA embedding space, enabling functional probing of cell types and brain regions.
Multi-stage brain tumor segmentation using Pathology-Aware Temporal Calibration (PA-TCNet) with physiological consistency constraints across temporal sequences for biologically plausible predictions.
Triple-phase multimodal framework for medical image classification — combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. Validated on microbial keratitis…
Frame neural quantum state optimization as reinforcement learning for scalable wavefunction approximation.
BehaviorVLM methodology - unified finetuning-free behavioral understanding using VLMs with quantum-dot-grounded pose estimation and LLM-based behavioral reasoning
Connectome rate operator analysis methodology — understanding how degree, weight govern gross response while exact wiring governs input routing. Use when analyzing complete connectomes, studying mushroom body function, or modeling neural network response…
Hybrid Classical-Quantum pipeline for Alzheimer's classification using supervised β-VAE and quantum kernels (arXiv:2606.14194)
OmniNeuro multimodal HCI framework for explainable BCI feedback — integrates Physics (Energy), Chaos (Fractal Complexity), and Quantum-Inspired uncertainty modeling to transform BCI from silent decoder to transparent feedback partner. Use when designing…
PRISM framework for cross-subject EEG emotion recognition using prioritized channel importance and semi-supervised domain adaptation. Differentiable channel weighting via lightweight expert ensemble plus confidence-filtered pseudo-labels for label-efficient…
Quantum-limited information capacity analysis for magnetoencephalography (MEG) and brain imaging. Derives fundamental bounds combining Planck's constant, metabolic power, and geometric constraints. Use when analyzing quantum limits in neuroimaging, computing…
Quantum-tunnelling oscillator model as universal dynamical engine for quantum cognition — models optical illusion perception and group decision making as quantum-mechanical agents with context-dependent state transitions, networked into quantum-cognitive…
SPIDER: Non-parametric frequency-domain framework for recovering directed brain connectivity from incomplete asynchronous recordings. Stitches power-spectra across sessions. Activation: effective connectivity, directed information flow, SPIDER, brain…
Free-probability framework for analyzing stationary covariance spectra in non-normal random recurrent neural networks. Derives closed functional equations for moment generating functions and analyzes tail eigenvalue behavior in critical regimes.…
Stochastic Graph Heat Modelling methodology for brain connectivity estimation. Uses noise-driven heat diffusion on graphs to estimate directed, multivariate, dynamic, model-based connectivity from neurophysiological data. Extends traditional coherence methods…
Subcortical shape variations and their associations with cognition across the 8th decade of life. Longitudinal study using neuroimaging and cognitive data from Lothian Birth Cohort 1936. Analyzes heterogeneous morphological trajectories in hippocampus,…
Validation framework for brain-encoding models like TRIBE — testing whether predicted fMRI signals correlate with behavioral engagement metrics. Use when evaluating brain-encoding models, testing fMRI predictions against behavioral data, or validating neural…
Wavelet Scattering Transform (WST) framework for interpretable schizophrenia biomarker discovery and classification from resting-state EEG. Multi-order scattering coefficients capture cross-frequency coupling and amplitude modulation dynamics, achieving…
General-purpose verification framework using probabilistic logit expectation for continuous scoring, enabling multi-dimensional scaling of verification along granularity, repeated evaluation, and criteria decomposition.
LLM-as-a-Verifier general-purpose verification framework using probabilistic verification and multi-round self-correction for improving LLM output reliability across reasoning, coding, and mathematical tasks.
NeuroCogMap framework for mapping cognitive functions in LLMs using neuroscience-inspired methodology. Analyzes hallucination, bias, refusal, sycophancy, and memory capabilities via parcel-functional annotation and cross-model functional correspondence.
Online safety monitoring methodology for LLMs at deployment time. Uses verifier signals from external models with calibrated thresholding via risk control to raise alarms when safety can no longer be assumed. Use when deploying LLMs and needing real-time…
Gamified methodology for detecting and fixing secure coding drift in LLM-assisted post-quantum cryptography development. Identifies gradual degradation of secure coding practices from sustained reliance on LLM-generated code in security-critical domains.
Learning biophysical Hodgkin-Huxley models from extracellular MEA data for precise neurostimulation prediction
Source text: Chinese
Coherence law for trainability in noisy equivariant quantum neural networks. Proves that readout-visible sector coherence determines gradient survival under decoherence, not just symmetry structure.
Holistic pulse synthesis methodology for quantum algorithms that bypasses discrete gate-stitching to compile algorithms directly into continuous compound pulse gadgets. Use when optimizing quantum circuits for trapped-ion or superconducting hardware, reducing…
Trainability-by-Design methodology for scalable Quantum Machine Learning using Dynamical Lie Algebra (DLA) constraints. Embeds group-theoretic geometric priors as structural regularizers to restrict DLA growth to polynomial regime, guaranteeing gradient-rich…
DRIADA Python toolkit for cross-scale analysis of single-neuron selectivity and population dynamics. Unifies neural signals (calcium imaging, spike trains, simulated networks) with time-aligned behavior in a shared data model for selectivity testing,…
Source text: Chinese
DRIADA - Python toolkit for cross-scale analysis of single-neuron selectivity and population dynamics. Enables unified analysis from single-cell selectivity to population-level dynamics in neuroscience experiments.
Equation Asymmetry Degree (EAD) framework for unifying secrecy and covertness in information-theoretic security. EAD = 1 - r/n governs both equivocation and detection error probability. Applies to MIMO wiretap, secure network coding, FRFT multi-angle…
Dynamical Lie Algebra (DLA) framework for navigating the expressivity-trainability paradox in QML - using group-theoretic geometric priors as structural regularizers to guarantee scalable, gradient-rich training landscapes.
Free probability approach to analyzing stationary covariance spectra of random recurrent neural networks. Derives closed functional equations for moment generating functions of limiting stationary covariance spectra with random non-normal Gaussian weights.
Krylov-Lie Algebras framework for Variational Quantum Algorithm (VQA) landscape analysis — provides numerically robust approximation of VQA reachable manifolds, weighted non-Haar variance formulas, and barren plateau mitigation via non-Haar corrections.
Mean-field theory for rich oscillatory dynamics in low-rank recurrent networks with activity-dependent adaptation. Analyzes how low-rank structure and adaptation interact to produce complex oscillatory and chaotic behavior in recurrent neural networks.
Deep neural network approaches for inverse design of superconducting radio-frequency (SRF) cavities and transmon qubits for bosonic quantum computation — mapping target device parameters to candidate geometries.