Design & UX

Four-generation framework for quantum biomedical sensors based on quantum resource utilization. Covers clinical translation challenges, noise limits, ensemble vs single-particle sensing. Use when: quantum biosensors, biomedical quantum sensing, clinical quantum sensors, quantum medical imaging sensors, biosensor generations, quantum resource biosensing, NV center biosensing, atomic magnetometer biomedical, quantum optical biosensing, macroscopic vs microscopic quantum sensing.

VQA methodology for dynamic portfolio optimization — sampling strategies (adaptive CVaR scheduling), optimizer scheduling (PSO+NFT hybrid), and hardware-aware ansatz design (data-guided colored layout, heavy-hex deep-chain layout). Activation: vqa portfolio, dynamic portfolio optimization, CVaR scheduling, hardware-aware ansatz, heavy-hex layout, PSO optimizer, quantum portfolio, 动态投资组合优化

Uncertainty-Guided Hypergraph Refinement (UGHR) methodology for medical image segmentation. Uses entropy-based uncertainty maps from coarse predictions to spatially guide targeted refinement in boundary/transition regions. Decouples foreground/background hyperedge prototypes to prevent noise propagation. Use when performing medical image segmentation with ambiguous boundaries, small lesions, or ill-defined edges.

γ_c-Peak covariant quantum error recovery methodology across organic qubit platforms — cryptochrome, MAO-A, Posner molecules, and radical-pair systems. Applicable to quantum brain models, biological quantum coherence, and organic quantum computing platforms.

Code-Modulated Motion Visual Evoked Potential (c-MVEP) methodology for brain-computer interfacing. Uses pseudo-random motion sequences instead of flickering for visual stimulation. Covers EEG-based BCI paradigms comparing c-MVEP, c-VEP, SSMVEP, SSVEP. Based on Scheppink et al. (arXiv: 2605.15801). Use when designing motion-based BCI systems, developing flicker-free visual stimulation paradigms, or comparing VEP-based BCI approaches. Activation: c-MVEP, motion VEP, code-modulated VEP, brain-co...

VQA methodology for dynamic portfolio optimization — sampling strategies (adaptive CVaR scheduling), optimizer scheduling (PSO+NFT hybrid), and hardware-aware ansatz design (data-guided colored layout, heavy-hex deep-chain layout). Activation: vqa portfolio, dynamic portfolio optimization, CVaR scheduling, hardware-aware ansatz, heavy-hex layout, PSO optimizer, quantum portfolio, 动态投资组合优化

Infinite-dimensional quantum controllability framework using von Neumann algebra techniques. Extends the finite-dimensional Lie algebra rank condition to bilinear quantum systems on infinite-dimensional Hilbert spaces by interpreting algebraic objects through affiliated operators. Use when: analyzing controllability of infinite-dimensional quantum systems, designing quantum control protocols for continuous-variable systems, generalizing Lie algebra rank conditions beyond finite dimensions, or...

Uncertainty-Guided Hypergraph Refinement (UGHR) methodology for medical image segmentation. Uses entropy-based uncertainty maps from coarse predictions to spatially guide targeted refinement in boundary/transition regions. Decouples foreground/background hyperedge prototypes to prevent noise propagation. Use when performing medical image segmentation with ambiguous boundaries, small lesions, or ill-defined edges.

TurboVLA architecture for real-time vision-language-action models achieving 32 Hz inference with <1 GB VRAM. Reformulates conventional V→L→A pathway as direct V+L→A mapping with lightweight bidirectional vision-language interaction. Use when building efficient robotic manipulation systems, real-time VLA policies, or low-resource embodied AI agents.

Thermocoherent framework for modeling information flow in neural matter. Heat flow couples to delocalized information flow carried by shared coherence. Use when: thermocoherent effects in neural systems, quantum cognition physical basis, relational resources in neural tissue (entanglement, discord, classical correlations), Mpemba-type thermal relaxation in cognition, cross-scale neural coordination, arXiv:2604.04069, quantum information flow in neural matter.

Programmable superconducting neuron with intrinsic in-memory computation and dual-timescale plasticity for ultra-efficient neuromorphic computing using Josephson junctions.

神经突触可塑性随机模型框架。基于STDP规则的突触权重演化数学模型，引入塑性核概念表示不同STDP规则，使用随机过程分析神经元-突触系统动力学。适用于计算神经科学、突触可塑性建模、STDP学习规则。触发词：突触可塑性、STDP、塑性核、突触权重、随机模型、synaptic plasticity、STDP、plasticity kernel、Hebbian learning。

STAMBRIDGE: Spectral-Temporal Amplitude-aware Mid-Feature Bridge for EEG Visual Decoding. Two-stage framework combining Spectral-Temporal Amplitude-aware Modulation (STAM) and Mid-Feature Semantic Bridge (MFSB) for zero-shot EEG-to-image retrieval and reconstruction. Achieves 34.50% Top-1 on THINGS-EEG. Activation: EEG visual decoding, EEG-to-image, zero-shot EEG retrieval, spectral-temporal modulation, brain-computer interface

Spiking neural network control framework based on the Free Energy Principle (FEP) and Active Inference. Neurons fire only when they reduce free energy of internal representation, achieving highly sparse activity with robust control. Matches performance of non-spiking frameworks while offering resilience against sensory noise, synaptic noise, delays, and neuron silencing. Use when designing spiking control systems, neuromorphic control algorithms, active inference with SNNs, energy-efficient r...

Derived from arXiv:2607.17951 - RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control

REVE (Representation for EEG with Versatile Embeddings) - EEG foundation model trained on 60,000 hours from 25,000 subjects with novel 4D positional encoding for arbitrary electrode configurations. Achieves SOTA on 10 downstream tasks. Activation triggers: EEG foundation model, REVE, versatile embeddings, 4D positional encoding, cross-dataset EEG, brain-computer interface.

Quantum divide and conquer methodology combining classical dynamic programming with quantum search to achieve improved exponential base for NP-hard combinatorial optimization. Parameterized hybrid algorithms with tunable quantum-classical balance. Use when designing quantum algorithms for NP-hard problems, implementing divide-and-conquer quantum strategies, or combining quantum search with classical DP.

Four-generation framework for quantum biomedical sensors based on quantum resource utilization. Covers clinical translation challenges, noise limits, ensemble vs single-particle sensing. Use when: quantum biosensors, biomedical quantum sensing, clinical quantum sensors, quantum medical imaging sensors, biosensor generations, quantum resource biosensing, NV center biosensing, atomic magnetometer biomedical, quantum optical biosensing, macroscopic vs microscopic quantum sensing.

PalmClaw: A Native On-Device Agent Framework for Mobile Phones - Large Language Model (LLM) agents have moved beyond generating responses to executing multi-step tasks by calling tools, observing the results, and it...

Optical neural networks using coherent transient dynamics in waveguide QED for all-optical neuromorphic computing

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 brain-computer interfaces with interpretability, neurofeedback systems, BCI sonification, multimodal BCI feedback, or quantum-inspired uncertainty in neural decoding (arXiv: 2601.00843)

Multi-objective optimization methodology for quantum computing workflows, combining compilation strategy selection, noise suppression, and error-mitigation. Based on QBalance framework (arXiv: 2605.02966) and action-space engineering for RL-based circuit routing. Use when: designing quantum compilation pipelines, optimizing NISQ device execution, selecting error-mitigation strategies, or formulating multi-objective quantum workflow problems.

Spatiotemporal TDANN for modeling self-organized MT direction selectivity maps in the dorsal stream. Uses 3D ResNet with Momentum Contrast (MoCo) self-supervised learning and biological spatial loss to produce direction-selective pinwheel structures matching macaque MT physiology. Use when modeling cortical topographic self-organization, dorsal stream computation, direction selectivity, or spatiotemporal contrastive learning for visual neuroscience. arXiv: 2605.11718 (q-bio.NC, cs.AI, cs.NE)....

利用MICrONS功能连接组学数据构建生物合理的RNN。整合皮质几何、解剖连接和功能关系作为归纳偏置，实现更有效的学习和收敛到生物计算的组织原则。