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Showing 10,441–10,464 of 29,837 skills
- Dream2learn Structured Generative DreamingDream2Learn (D2L) framework for continual learning using structured generative dreaming to create novel synthetic experiences from internal representations. Use when: (1) implementing continual learning systems; (2) addressing catastrophic forgetting; (3) generating synthetic training data; (4) expanding representation space through internal simulation; (5) achieving positive forward transfer in sequential tasks. Trigger words: Dream2Learn, D2L, structured dreaming, generative dreaming, conti...Votes: 0GitHub stars: 3
- Dose Efficient Quantum InterferometryDose-efficient quantum phase estimation methodology using sequential strategies in lossy optical interferometry for biological and medical imaging. Control-enhanced sequential strategies achieve superior quantum Fisher information per dose, approaching the quantum limit in dose-limited regimes.Votes: 0GitHub stars: 3
- Distributionally Robust ControlDesign and analyze distributionally robust control systems under uncertainty with incomplete distribution information. Covers Sinkhorn ambiguity sets, convexity analysis, weak compactness, tractability guarantees, and MPC approaches. Use when designing controllers for systems with uncertain probability distributions, implementing robust MPC, or analyzing worst-case performance under distributional ambiguity.Votes: 0GitHub stars: 3
- Distributed Variational Quantum OptimizationQESTO distributed variational quantum optimization methodology using entanglement-selective transport for graph-based discrete optimization. Requires only persistent pre-shared Bell pairs for remote operations, no non-local gates after initialization. Use when: distributed quantum optimization, variational quantum algorithms, QAOA alternatives, Bell pair communication, entanglement transport, graph optimization, Wang tiling problems.Votes: 0GitHub stars: 3
- Distributed Quantum Error CorrectionDesign and analyze distributed quantum error correction (QEC) systems using bivariate bicycle (BB) codes in modular quantum computing architectures. Covers qLDPC code partitioning across multiple processors, star network topology for inter-processor connectivity, BP+OSD decoding, and fault tolerance threshold analysis under circuit-level noise. Use when: designing modular quantum computers, implementing distributed QEC, partitioning qLDPC codes across processors, analyzing inter-processor ent...Votes: 0GitHub stars: 3
- Discrete Signaling Chaotic Regularization离散信号介导混沌正则化方法论。连接循环网络的微观混沌与神经表征的宏观几何，解释混沌网络如何维持平滑可微的群体编码。使用核方法+动态平均场理论，展示混沌诱导局部粗糙性但保持全局平滑性，产生幂律谱特征。适用于混沌SNN稳定性分析、神经表征几何、皮质记录谱分析。触发词：混沌网络、chaotic dynamics、neural representation、regularization、kernel method、mean-field theory、power-law spectrumVotes: 0GitHub stars: 3
- Diophantine Quantum OracleReversible quantum oracle construction for solving bounded Diophantine systems via amplitude amplification. Use when designing quantum algorithms for integer optimization, constraint satisfaction over bounded domains, or synthesizing arithmetic circuits for quantum oracles.Votes: 0GitHub stars: 3
- Wrpn Training And Inference Using Wide Reducedprecision Networks**arXiv ID:** 1704.03079 **Authors:** Asit Mishra, Jeffrey J Cook, Eriko Nurvitadhi, Debbie Marr **Published:** 2017-04-10T22:54:38Z **Abstract:** For computer vision applications, prior works have shown the efficacy of reducing the numeric precision of model parameters (network weights) in deep neural networks but also that reducing the precision of activations hurts model accuracy much more than reducing the precision of model parameters. We study schemes to train networks from scratch usin...Votes: 0GitHub stars: 3
- Graphpo Policy Optimization ReasoningGraph-based Policy Optimization (GraphPO) for reasoning models. Represents rollouts as DAGs, merges semantically equivalent paths, and improves advantage estimation variance through graph structure.Votes: 0GitHub stars: 3
- Coflow Scheduling OcsCoflow Scheduling in Multi-Core Optical Circuit Switching Networks with Performance Guarantee. Optimize parallel data flow coordination in distributed systems using optical circuit switching. Use for data center network optimization, coflow scheduling, and distributed job completion time reduction.Votes: 0GitHub stars: 3
- Ddd Microservice SimulatorDomain-Driven Design simulator for business logic-rich microservice systems. Isolates core business logic from communication and transactional infrastructure, evaluates identical application code under varying consistency guarantees and network constraints, and supports Sagas and Transactional Causal Consistency (TCC) transactional models. Activation: DDD microservices, saga pattern, TCC, transactional consistency, microservice simulation, aggregate modeling, distributed consistency validatio...Votes: 0GitHub stars: 3
- Dbnn Spike ClassificationDBNN (Deep Binarized Neural Network) for hardware-efficient neural spike classification with multiplier-free inference. Achieves 98.7% accuracy with 0.014 mm² area and 122 nW power at 20 kHz. Uses sign-controlled accumulation and bit-wise logic for implantable brain-computer interfaces. Activation: DBNN, spike sorting, binarized neural network, brain-computer interface, FPGA implementation, ASIC design, neural decoding, implantable devices.Votes: 0GitHub stars: 3
- Research Skill Duplicate PreventionGuidelines and patterns for preventing duplicate entries in INDEX.md during automated research workflows. Provides detection methods, prevention strategies, and resolution procedures for handling duplicate skill entries in knowledge repositories.Votes: 0GitHub stars: 3
- Datacenter Ai Workload Power Planning[TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]Votes: 0GitHub stars: 3
- Astro Gnn Anomaly Detection CpsASTRO (Adaptive Spatio-Temporal Reinforcement Optimization) for GNN-powered anomaly detection in Industrial IoT and Cyber-Physical SystemsVotes: 0GitHub stars: 3
- A Datadriven Framework For Identifying Investment Opportunities In Private Equity**arXiv ID:** 2204.01852 **Authors:** Samantha Petersone, Alwin Tan, Richard Allmendinger, Sujit Roy, James Hales **Published:** 2022-04-04T21:28:34Z **Abstract:** The core activity of a Private Equity (PE) firm is to invest into companies in order to provide the investors with profit, usually within 4-7 years. To invest into a company or not is typically done manually by looking at various performance indicators of the company and then making a decision often based on instinct. This process ...Votes: 0GitHub stars: 3
- Data Driven Reachability Matrix PerturbationData-driven reachability analysis framework using matrix perturbation theory. Provides Cai-Zhang bounds for matrix zonotopes and constrained matrix zonotopes. Enables efficient reachable-set propagation with coefficient-space approximation. Use for: safety verification of uncertain systems, robust control synthesis, formal verification of dynamical systems, computational reachability analysis.Votes: 0GitHub stars: 3
- Cvar Glidepath Target Date FundDeclining CVaR glidepath framework for target-date fund design. Controls portfolio risk through explicit Conditional Value-at-Risk glidepaths linked to pension-design inputs. Use when: target-date fund design, CVaR portfolio optimization, pension fund glidepath, retirement planning, declining risk budget, explicit return objective portfolio.Votes: 0GitHub stars: 3
- Covert Quantum Computing CrosstalkFramework for analyzing and ensuring computational covertness in multi-tenant quantum computers, accounting for crosstalk-based side channels and adversarial detection via quantum-strategy framework.Votes: 0GitHub stars: 3
- Core Brain Lesion SegmentationConcept-Reasoning Expansion framework for continual brain lesion segmentation in MRI. Combines visual perception with structured medical concepts to handle pathological heterogeneity and prevent catastrophic forgetting. Activation: brain lesion segmentation, continual learning, medical image segmentation, concept-reasoning, CoRE, MRI analysis.Votes: 0GitHub stars: 3
- Craft ClCRAFT: Forgetting-Aware Intervention-Based Adaptation for continual learning. Avoids weight updates by learning low-rank interventions on hidden representations. Routes in representation space using KL divergence to decide between adaptation and routing. Use when: LLM continual learning, intervention-based adaptation, catastrophic forgetting mitigation, representation-space routing.Votes: 0GitHub stars: 3
- Complex Valued Neuromorphic Magnitude PhaseComplex-valued neural network with magnitude-phase decomposition for event-driven neuromorphic learning, enabling efficient spiking computation with rich representational capacityVotes: 0GitHub stars: 3
- Commuting Pauli ParallelizationMethodology for optimizing parallel execution of commuting Pauli Product Rotations in fault-tolerant quantum computation. Use when: (1) compiling quantum programs to Pauli Product Measurements (PPMs), (2) reducing circuit depth in surface code architectures with lattice surgery, (3) scheduling commuting quantum operations under hardware port constraints, (4) optimizing logical-layer quantum compilation. Keywords: Pauli Product Rotation, commuting groups, lattice surgery, surface code, circuit...Votes: 0GitHub stars: 3
- Cogeegagent Autonomous Cognitive Eeg AnalysisCogEEGAgent methodology for autonomous cognitive EEG analysis with grounded execution and selection-aware verification. Uses MNE-Python framework with LLM agents for flexible language understanding while maintaining fail-closed control over inference and release. Provides auditable automation framework for cognitive-EEG workflows with participant-disjoint confirmation and capability hazard blocking.Votes: 0GitHub stars: 3