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Testing & QA
Unit tests, integration tests, E2E, QA, validation, and test automation
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- Adaptive Directional Gradient QcForward gradient estimation methodology for training parameterised quantum circuits (PQCs). Introduces QUIVER adaptive optimiser that recovers SPSA, random coordinate descent, and parameter-shift rule as limiting cases. Enables efficient training of 60-qubit quantum neural networks.Votes: 0GitHub stars: 3
- Functional Whole Brain ModelsFunctional Whole-Brain Models (FWBM) methodology bridging bottom-up whole-brain modeling and top-down neuroconnectionism. Combines biophysically detailed simulations with functional-performance-driven deep neural networks. Use when: designing brain-scale computational models, integrating structure and function in neural modeling, building neuroconnectionist models with biological grounding, developing hybrid brain models that achieve both biological fidelity and functional competence. Activat...Votes: 0GitHub stars: 3
- Thsdk StockFetch stock market data via thsdk: realtime quotes, intraday data, historical K-lines for A-shares, HK/US stocks, futures, forex.Votes: 0GitHub stars: 3
- Stress Test Resilience Esg FragilityESG and joint fragility analysis in equity markets using stress-amplified resilience framework. Analyzes clustered fragility across downside returns, volatility spikes, and deteriorating tradability. Use when: ESG investing, stress testing, portfolio resilience, joint fragility, cofragility analysis, equity market risk, multi-factor risk, downside risk, liquidity risk.Votes: 0GitHub stars: 3
- Esg Joint Fragility Equity MarketsFramework for analyzing ESG's association with joint fragility in equity markets — clustered downside risk across losses, volatility spikes, and illiquidity using cofragility state detection.Votes: 0GitHub stars: 3
- Distributional Portfolio OptimizationDistributional Portfolio Optimization (DPO) unified framework — organizing Bayesian, robust, chance-constrained, stochastic-allocation, and distributional RL portfolio methods through joint coupling Gamma_theta(dw,dr). Includes Wasserstein-CVaR duality, credible-radius calibration, and distributional Bellman contraction. Activation: distributional portfolio optimization, DPO, Wasserstein DRO, Bayesian portfolio, CVaR, credible radius, distributional reinforcement learning.Votes: 0GitHub stars: 3
- FinanceEmpirical investigation of long-range dependence (LRD) in financial markets and evaluation of deep generative models' ability to reproduce such temporal structures across equity, commodity, and energy sectors.Votes: 0GitHub stars: 3
- Exact Ensemble Controllability Neural Differential EquationsExact ensemble controllability for neural differential equations via neural interpolation - constructive solution for steering multiple initial states to corresponding target states with a single set of control parameters in neural dynamics systems.Votes: 0GitHub stars: 3
- Dual Envelope MpcNonlinear Model Predictive Control with dual-envelope constraints for distributed drive systems. Use when: vehicle dynamics control, MPC implementation, envelope-based stability analysis, autonomous drifting, distributed drive electric vehicles, phase plane analysis, saddle point modeling, or constrained nonlinear control systems.Votes: 0GitHub stars: 3
- Domain Saturation ValidationComplete validation workflow for automated research when domain saturation is detected (>80% paper coverage). Verifies synchronization across local skills, repository, INDEX.md, knowledge graph, and Obsidian notes. Activation: domain saturation, research validation, sync verification, complete validationVotes: 0GitHub stars: 3
- Distributed Optimization Control Vpp分布式优化控制架构用于逆变器接口虚拟电厂的大信号稳定性分析方法论。结合优化理论、控制理论、分布式系统设计，实现DER（分布式能源资源）的二次控制。Activation: distributed control, virtual power plant, VPP, inverter control, DER control, optimization-based control, large-signal stability.Votes: 0GitHub stars: 3
- Decoding Encoding Alignment CritiqueCritical analysis framework for brain-model alignment methodology. Demonstrates that representational similarity analysis (RSA) and decoding-based alignment metrics are fundamentally insensitive to encoding manifold topology. Similar decoding behavior and high representational alignment can arise from small, non-representative neuron subpopulations. Use when: evaluating brain-DNN alignment, RSA/DSA methodology, encoding vs decoding analysis, neural representation comparison, brain-model simil...Votes: 0GitHub stars: 3
- Streamindex Csa TopkMemory-bounded compressed sparse attention via streaming chunked top-k. Eliminates OOM in DeepSeek-V4 CSA indexer by avoiding full score tensor materialization.Votes: 0GitHub stars: 3
- Dark Signals Brain HamiltonianDark Signals framework — physics-informed complex-valued brain network dynamics using Hamiltonian mechanics. Augments observed fMRI/EEG signals with latent conjugate momenta via Hilbert transform, yielding Schrödinger-like equations for whole-brain dynamics. Activation: dark signals, hamiltonian brain, complex-valued brain dynamics, hilbert transform brain, brain network prediction, effective connectivityVotes: 0GitHub stars: 3
- Cusped Singularity Mmo AnalysisGeometric singular perturbation analysis of mixed-mode oscillations (MMOs) in inhibitory neural networks using cusped singularities. Activation triggers: mixed-mode oscillations, MMO, cusped singularity, slow-fast neural system, mutual inhibition oscillation, singular perturbation neural, blow-up method neural, neural oscillation mechanism, slow-fast system analysis.Votes: 0GitHub stars: 3
- Cumulant Order Block Sparse AttentionCOBS - block sparse attention selector that stores a compressed SECOND-order (cumulant) statistic per block instead of only first-order (attention mass), closing the gap to dense attention at ~15x less KV-cache read traffic. Use when building/improving block-sparse attention (NSA-style) for long-context LLMs and existing selectors under-select.Votes: 0GitHub stars: 3
- Contravariance Theory Strong AlignmentContravariance Theory methodology — formal proof that minimal DNN solutions to sufficiently hard tasks exhibit strong alignment: weak alignment of representations guarantees strong alignment of privileged axes, and alignment zippers up the network hierarchy, proving convergent evolution is inevitable.Votes: 0GitHub stars: 3
- Continual Robot Policy Variational Neural DynamicsContinual robot policy learning framework using Variational Neural Dynamics. Combines analytical physics prior with neural residual for unmodeled effects. Recurrent encoder infers hidden conditions from recent interaction. Policy learning via differentiable simulation with sampled dynamics. Deployment uses online condition inference for recurring dynamics recovery. Activation: continual robot learning, variational dynamics, hidden condition, physics prior, differentiable simulation, quadrotor...Votes: 0GitHub stars: 3
- Causal Data Management Ecosystem Agentic AiCausal World System for agentic AI decision making.Votes: 0GitHub stars: 3
- Bus Brain Inspired Unsupervised Self ReflectionBUS: Brain-Inspired Unsupervised Self-Reflection for VLM reasoning. Uses backward prediction (neuroscience concept) to enable label-free self-reflective reasoning in Vision-Language Models. Compatible with SFT and RL, tested on 8 benchmarks. Activation: BUS, backward prediction, unsupervised self-reflection, VLM reasoning, neuroscience-inspired training.Votes: 0GitHub stars: 3
- Brain Digital Twins Execution SemanticsBrain digital twins execution semantics framework bridging computational modeling and neurobiological dynamics. Covers physically constrained executability, end-to-end workflow preservation, and neuromorphic implementation. Activation: brain digital twins, execution semantics, neuro-neuromorphic, computational neuroscience.Votes: 0GitHub stars: 3
- Brain Digital Twins Execution Semantics V4Brain digital twins execution semantics survey bridging computational neuroscience to neuromorphic systems - arXiv:2604.13574 (April 2026). Covers physically constrained executability taxonomy, execution regimes, hybrid-time correctness, and neuro-neuromorphic physical systems. Supersedes v3 with systems/runtime perspective.Votes: 0GitHub stars: 3
- Biological Plausibility Assessment SnnAutomated framework for assessing biological plausibility of spiking neuron models using Izhikevich firing pattern classification. Use when evaluating spiking neuron models, comparing neuromorphic designs, or quantifying how well artificial neurons replicate biological behavior.Votes: 0GitHub stars: 3
- Bilinear Gating Motor Primitives Dendritic ComputationBilinear gating methodology linking dendritic coincidence detection to goal-directed adaptation. Motor cortex neurons encode goal information in burst fraction (not firing rate), implementing bilinear gate G(g)·Y(s) where goal and state inputs multiply via dendritic coincidence detection. Supports zero-shot generalization and rapid online adaptation. Accepted arXiv 2606.10891.Votes: 0GitHub stars: 3