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Claude Skills by hiyenwong
github.com/hiyenwong9,934 skills5 installs19,223 views
- Inverse Born Rule FallacyCritical analysis methodology for quantum data encoding — identifies how naive amplitude encoding (psi=sqrt(P)) abelianizes the Hilbert space and fails to achieve genuine quantum advantage in QML/finance. Advocates for Dynamical Hamiltonian Encoding (DHE) where data generates non-commutative evolution.Votes: 0GitHub stars: 3
- Market Informedness Rl Market MakingMarket making with heterogeneous agents and reinforcement learning — MAPPO algorithm with finite-horizon stability guarantees for Hawkes market-taker processes. Shows profitability increases with market informedness.Votes: 0GitHub stars: 3
- Markets Hard To Predict FrameworkMarket predictability framework distinguishing epistemic uncertainty (reducible) from aleatoric uncertainty (irreducible) in financial markets. Based on the thesis that markets are not random but hard to predict — with profound implications for investment strategy, risk management, and portfolio construction.Votes: 0GitHub stars: 3
- Modeling The Telemarketing Process Using Genetic Algorithms And Extreme Boosting Feature Selection And Costsensitive Analytical Approach**arXiv ID:** 2310.19843 **Authors:** Nazeeh Ghatasheh, Ismail Altaharwa, Khaled Aldebei **Published:** 2023-10-30T08:46:55Z **Abstract:** Currently, almost all direct marketing activities take place virtually rather than in person, weakening interpersonal skills at an alarming pace. Furthermore, businesses have been striving to sense and foster the tendency of their clients to accept a marketing offer. The digital transformation and the increased virtual presence forced firms to seek novel m...Votes: 0GitHub stars: 3
- Portfolio Optimization Mean Variance SpectrumPortfolio Optimization with Mean-Variance-Spectrum PreferencesVotes: 0GitHub stars: 3
- Portfolio Selection Belle Art EconomicsPortfolio Selection is More of a Belle Art Than Economics or FinanceVotes: 0GitHub stars: 3
- Psaas Portfolio Selection For Automated Algorithm Selection In Blackbox Optimization**arXiv ID:** 2310.10685 **Authors:** Ana Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Jankovic, Ana Nikolikj, Urban Skvorc, Peter Korosec, Carola Doerr, Tome Eftimov **Published:** 2023-10-14T12:13:41Z **Abstract:** The performance of automated algorithm selection (AAS) strongly depends on the portfolio of algorithms to choose from. Selecting the portfolio is a non-trivial task that requires balancing the trade-off between the higher flexibility of large portfolios with the increas...Votes: 0GitHub stars: 3
- Qadqn TradingQuantum Attention Deep Q-Network (QADQN) for financial market prediction and optimal trading strategy development. Variational quantum circuit inside deep Q-learning framework, achieving superior risk-adjusted returns (Sortino ratio 1.28) with real transaction cost modeling. Use when: quantum reinforcement learning trading, quantum attention trading strategy, financial market prediction quantum, QADQN algorithm, quantum-enhanced RL for finance.Votes: 0GitHub stars: 3
- Recap Regime Adaptive PortfolioRegime-aware Continual Adaptive Portfolio management (ReCAP) — integrating continual learning into portfolio management via adaptive regime detection, policy libraries, and regime-gated policy combination. Accepted by KDD 2026. Activation: regime detection, portfolio management, continual learning, adaptive trading, ReCAP, market regime, policy library, regime shift.Votes: 0GitHub stars: 3
- Stock AnalysisComprehensive 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.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
- 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
- Tight Stability Convergence And Robustness Bounds For Predictive Coding Networks**arXiv ID:** 2410.04708 **Authors:** Ankur Mali, Tommaso Salvatori, Alexander Ororbia **Published:** 2024-10-07T02:57:26Z **Abstract:** Energy-based learning algorithms, such as predictive coding (PC), have garnered significant attention in the machine learning community due to their theoretical properties, such as local operations and biologically plausible mechanisms for error correction. In this work, we rigorously analyze the stability, robustness, and convergence of PC through the lens ...Votes: 0GitHub stars: 3
- Trading Inference Time Adversarial RobustnessMethodology for trading inference-time compute to improve adversarial robustness in LLMs through repeated sampling and output filtering.Votes: 0GitHub stars: 3
- Unified Multimodal Financial Ai FrameworkUnified multi-modal framework integrating PPO robo-advisory, HFT prediction, in-context investment advisory, game-theoretic banking, and cross-modal sentiment analysis. Use when: unified financial AI systems, multi-domain financial AI, robo-advisory optimization, high-frequency trading, competitive banking strategy, cross-modal financial sentiment.Votes: 0GitHub stars: 3
- Yet Another But More Efficient Blackbox Adversarial Attack Tiling And Evolution Strategies**arXiv ID:** 1910.02244 **Authors:** Laurent Meunier, Jamal Atif, Olivier Teytaud **Published:** 2019-10-05T10:36:47Z **Abstract:** We introduce a new black-box attack achieving state of the art performances. Our approach is based on a new objective function, borrowing ideas from $\ell_\infty$-white box attacks, and particularly designed to fit derivative-free optimization requirements. It only requires to have access to the logits of the classifier without any other information which is a m...Votes: 0GitHub stars: 3
- Your Network May Need To Be Rewritten Network Adversarial Based On Highdimensional Function Graph Decomposition**arXiv ID:** 2405.03712 **Authors:** Xiaoyan Su, Yinghao Zhu, Run Li **Published:** 2024-05-04T11:22:30Z **Abstract:** In the past, research on a single low dimensional activation function in networks has led to internal covariate shift and gradient deviation problems. A relatively small research area is how to use function combinations to provide property completion for a single activation function application. We propose a network adversarial method to address the aforementioned challenges...Votes: 0GitHub stars: 3
- Find SkillsHelps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.Votes: 0GitHub stars: 3
- Finite Shot Quantum Moment EstimationFinite-shot moment estimation methodology for quantum metrology — bias-corrected estimators, calibration curve analysis, and sensitivity corrections for quantum parameter estimation beyond the Cramer-Rao bound. Use when working with quantum metrology, parameter estimation, moment estimators, quantum Cramer-Rao bound, finite measurement number, bias correction, or calibration curves in quantum sensing and quantum information tasks.Votes: 0GitHub stars: 3
- Finite Temp Quantum KrylovFinite-temperature quantum Krylov method for computing thermal properties of quantum many-body systems from real-time overlaps. Use when analyzing quantum many-body systems at finite temperatures, computing thermal observables, or avoiding thermal state preparation in quantum simulations.Votes: 0GitHub stars: 3
- Finite Time Reachability Partial ControlFinite-time reachability control for constrained nonlinear systems with partial loss of control authority. Addresses driving system to target state in finite time despite partial actuator failures. Use when designing fault-tolerant control, handling actuator failures, reachability under constraints, or nonlinear control with reduced control authority.Votes: 0GitHub stars: 3
- Firing Rate Nn Mpc ImplementationFiring rate neural network implementations of Model Predictive Control (MPC) for real-time control applications. Activation: firing rate MPC, neural network control, model predictive control, real-time neural control, rate-coded neural MPC.Votes: 0GitHub stars: 3
- First In Human Quantum Entanglement ImagingFirst-in-human quantum entanglement imaging methodology using J-PET plastic scintillator scanner. Measures polarization correlations of annihilation photons from positron-electron annihilation in vivo for clinical diagnostics. Use when: quantum PET imaging, entanglement-based medical imaging, J-PET scanner design, polarization-correlated tomography, quantum entanglement degree as biomarker, 68Ga radiopharmaceutical quantum imaging.Votes: 0GitHub stars: 3
- Fisher Glasses Tail Certified MetrologyTail-certified quantum metrology for quenched sensors — Fisher-zero integrability transition, no-go theorem on averaged Fisher data, universal design laws (safe windows, nondegenerate portfolios, Fisher reserves, Fisher-cut criteria). Activation: quantum metrology, Fisher information, quenched environments, tail certification, NV centers, superconducting qubits, Fisher glass, QFI certificationVotes: 0GitHub stars: 3
- Fits Interpretable Spiking NeuronFiTS (Frequency Selectivity and Temporal Shaping) interpretable spiking neuron model. Factorizes temporal computation into Frequency Selectivity and Temporal Shaping within each neuron. Use when: spiking neural networks, interpretable neurons, temporal processing, frequency selectivity, temporal shaping, LIF neuron improvement, auditory processing, neuron-level interpretability, group-delay modulation, subthreshold magnitude response, feedforward SNNs, spike timing.Votes: 0GitHub stars: 3
- Fits Interpretable Spiking NeuronsFiTS (Frequency Selectivity and Temporal Shaping) spiking neuron methodology for interpretable SNN temporal processing. Factorizes neuron-level temporal computation into Frequency Selectivity (FS) and Temporal Shaping (TS) modules. FS parameterizes target frequency as maximizer of subthreshold magnitude response; TS reshapes when frequency components contribute to membrane voltage accumulation through group-delay modulation. Use when: designing interpretable SNNs for audio/temporal tasks, fre...Votes: 0GitHub stars: 3
- Fixed Point Compositionality Low Rank Gluing固定点组合性低秩胶合理论框架。研究结构模块化如何支持抑制主导阈值线性网络的功能组合性，引入低秩胶合规则实现不动点的组合性。Votes: 0GitHub stars: 3
- Fixed Point Compositionality Low Rank GluingsMathematical framework for compositional dynamics in threshold-linear networks via low-rank gluing rules. Use when studying modular network assembly, fixed point decomposition, compositional limit cycles, or engineering networks with predictable attractor repertoires.Votes: 0GitHub stars: 3
- Flare Plus Plus Low Rank Attention RoutingFLARE++ for dynamic low-rank attention routing.Votes: 0GitHub stars: 3
- Flexibrain Resolution Agnostic Fmri EncodingFlexiBrain - Resolution-agnostic voxel-level encoding framework for native fMRI based on Mamba-JEPA. Bypasses destructive spatial standardization, reduces preprocessing costs, and accelerates robust voxel-level fMRI foundation models.Votes: 0GitHub stars: 3
- Flow Based Connectivity DistributionFlow-based probabilistic inference for neural connectivity distribution. Uses normalizing flows to model the distribution of possible brain connectomes, enabling uncertainty quantification in connectivity estimation. Supports downstream tasks: network analysis, disease classification, and intervention planning with principled uncertainty estimates.Votes: 0GitHub stars: 3
- Flow Matching Brain DynamicsFlow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics — compositional conditional generation of neural time series using continuous normalizing flows, enabling zero-shot generalization to novel experimental conditionsVotes: 0GitHub stars: 3
- Flow Matching In Context Brain DynamicsFlow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics methodology — per-timestep conditioned diffusion transformer for generating realistic fMRI during unseen cognitive tasks using compositional language and spatial priors.Votes: 0GitHub stars: 3
- Flow Matching In Context Priors Brain DynamicsFlow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics - First generative model of whole-cortex fMRI dynamics for unseen cognitive tasks, advancing counterfactual neuroscience and data-driven experimental design.Votes: 0GitHub stars: 3
- Flowedit Associative Memory Lifelong PronunciationFlowEdit introduces lifelong adaptation for frozen TTS models using Modern Hopfield Networks as content-addressable episodic memory, enabling pronunciation corrections without weight updates.Votes: 0GitHub stars: 3
- Flux Longitudinal Flow MatchingGeometry-aware longitudinal flow matching framework for unpaired biological snapshot data. FLUX learns data-dependent metrics, constructs manifold-aware conditional paths, and uses mixture-of-experts velocity fields for joint transport modeling and unsupervised regime discovery. From Ortega Caro et al. 2026 (arXiv:2605.08648). Use when: modeling unpaired longitudinal biological data, flow matching on manifolds, neural dynamics trajectory reconstruction, cell differentiation modeling, calcium ...Votes: 0GitHub stars: 3
- Fluxonium Scalable ArchitectureScalable fluxonium quantum processor architecture using tunable-coupler unit cells. Achieves 99.9% CZ gate fidelity and demonstrates 22-qubit GHZ state generation. Alternative to transmon-based superconducting quantum computers. Keywords: fluxonium, superconducting qubits, tunable coupler, scalable quantum processor, CZ gate, high fidelity, quantum hardware.Votes: 0GitHub stars: 3
- Fly Goal Normalization Fc2Analysis of Drosophila FC2 circuit mechanism showing that goal maintenance uses normalization rather than winner-take-all selection, with global inhibition from FB5A neurons keeping a single clean activity bump rather than actively choosing between competing goals.Votes: 0GitHub stars: 3
- Fmri Dictionary Learning Optimal TransportNovel approach to dictionary learning on fMRI data that explicitly accounts for individual brain geometry variability using optimal transport (Fused Gromov-Wasserstein distance) with amortized optimization for computational efficiency.Votes: 0GitHub stars: 3
- Fmri Gesture ReconstructionfMRI2GES: Dual brain decoding alignment framework for co-speech gesture reconstruction from fMRI signals. Maps brain responses to external stimuli and decodes gestural behavior through dual-alignment brain decoding. Activation: fMRI gesture reconstruction, brain-to-gesture decoding, fMRI2GES, co-speech gesture brain decoding, dual brain decoding alignment.Votes: 0GitHub stars: 3
- Forge Memory Efficient Llm TrainingFused On-Register Gradient Elimination for memory-efficient LLM training. Folds optimizer step into backward pass, applies tile-by-tile in registers, eliminating materialized gradients.Votes: 0GitHub stars: 3
- Formalizing Binding ProblemInformation-theoretic formalization of the binding problem and probing method for measuring binding information in Vision Transformers and neural representations.Votes: 0GitHub stars: 3
- Formally Guaranteed Control AdaptationControl adaptation with formal guarantees for ODD-resilient autonomous systems. Combines barrier functions, adaptive control, and reachability analysis to provide safety certificates during online controller parameter adjustment. Use when designing adaptive controllers with runtime safety guarantees, ODD-resilient autonomous systems, barrier function-based adaptive control, reachability analysis for control systems, verified parameter adaptation. Trigger: control adaptation, ODD resilience, b...Votes: 0GitHub stars: 3
- Foundation Models Brain BiomarkerFoundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity. Use when: building neurological biomarker discovery pipelines, applying foundation models to fMRI/EEG data, analyzing dynamic functional connectivity for disease detection, developing robust cross-subject biomarkers. Triggers: brain biomarker foundation model, dynamic functional connectivity biomarker, neurological disorder detection, robust biomarker discovery, fMRI foundation ...Votes: 0GitHub stars: 3
- Fourier Lcu Quantum OptimizationFourier-based Linear Combination of Unitaries (LCU) methodology for efficient quantum circuit decomposition in optimization algorithms. Covers ancilla-free LCU constructions, Fourier decomposition of diagonal/non-diagonal unitaries, formal connection to Lagrangian relaxation, and hardware-friendly gate layer simplification. Activation: LCU, linear combination of unitaries, Fourier quantum, quantum optimization decomposition, constraint penalty, XY-mixer, cardinality constraint, ancilla-free q...Votes: 0GitHub stars: 3
- Foveated Dynamic Token SelectionFoveation-guided dynamic token selection for robust and efficient vision transformers. Inspired by human visual system foveated sampling + eye movements. Use when building efficient ViTs, dynamic token pruning/selection, or robustness-to-noise/adversarial without explicit robust training.Votes: 0GitHub stars: 3
- Fped Moe Brain DecodingFunctional-Network Prior-Guided Mixture-of-Experts (MoE) framework for interpretable brain decoding from fMRI. Uses brain network topology as expert priors with adaptive routing for visual semantic reconstruction.Votes: 0GitHub stars: 3
- Fpga Quantum DecoderDesign FPGA-based neural network decoders for real-time quantum error correction (QEC) in surface code architectures. Covers hardware-software co-design, deterministic low-latency decoding under 1 microsecond, NN decoder implementation on FPGA, closed-loop feedback control, and mid-circuit Pauli-frame correction for non-Clifford logical circuits. Use when building real-time QEC systems, implementing FPGA-based decoders, designing low-latency quantum control hardware, or optimizing surface cod...Votes: 0GitHub stars: 3
- Fpga Quantum Error DecoderFPGA-based real-time quantum error correction decoding architecture. Combines hardware-integrated NN decoders on FPGA with superconducting quantum processors for low-latency closed-loop QEC. Use when: (1) Designing real-time QEC control systems, (2) Implementing FPGA-based syndrome decoders, (3) Building low-latency feedback loops for fault-tolerant quantum computing, (4) Analyzing closed-loop latency budgets for QEC cycles, (5) Implementing mid-circuit Pauli-frame corrections in non-Clifford...Votes: 0GitHub stars: 3
- Fqpdr Medical DetectionFederated Quantum Neural Network methodology for privacy-preserving medical image diagnosis. Combines federated learning (FL) with quantum neural networks (QNN) for early disease detection without sharing patient data. Addresses medical data privacy constraints while maintaining diagnostic accuracy. Covers quantum kernel methods for medical foundation model embeddings, tensor-network frontends for federated medical diagnosis, and quantum-enhanced medical image classification. Use when: federa...Votes: 0GitHub stars: 3