All categories
DevOps & Cloud
Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
- 20,289
- 846
Security grades appear on each card once the skill has been scanned. Newly imported skills may briefly show without a grade until the backfill job runs.
Open in full browserBrowse devops & cloud skills
Showing 7,105–7,128 of 20,289 skills
- Hybrid Quantum Neighborhood SelectionHybrid Quantum Neighborhood Selection (HQNS) — resource-efficient framework for large-scale combinatorial optimization. Decomposes dense QUBO into bounded-width quantum subproblems via stochastic frontier selection. Preserves 99.99% solution quality while reducing wall-clock time 94.91%, CPU 64.68%, memory 88.61%. QPU execution bounded at 6-7s regardless of global problem scale.Votes: 0GitHub stars: 3
- Diamond Quantum NetworksQuantum networking using diamond color defects (NV/SiV centers) for scalable quantum communication, distributed quantum computing, and sensing. Comprehensive methodology covering optical properties, spin-qubit control, spin-photon interfaces, nanophotonic integration, and metropolitan-scale quantum network demonstrations. Use when building quantum networks, designing quantum repeaters, implementing spin-photon interfaces, or evaluating solid-state qubit platforms for quantum communication. Ac...Votes: 0GitHub stars: 3
- Cv Qnn Edge Medical ImagingParameter-efficient continuous-variable photonic quantum neural networks for edge medical AI. Room-temperature quantum ML for medical image classification with 40-45% parameter reduction. Covers CV-QNN architecture simplification, barren plateau mitigation, and edge deployment strategies.Votes: 0GitHub stars: 3
- Cv Qnn Edge AiParameter-efficient Continuous-Variable photonic Quantum Neural Networks for edge AI deployment. Simplified CV-QNN architecture reduces trainable parameters by 40-45% while maintaining or exceeding classical baseline performance. Barren plateau mitigation via dimensionality reduction and encoding restriction. Use when: building quantum machine learning models for edge deployment, optimizing CV-QNN architectures, mitigating barren plateaus, parameter-efficient quantum classification.Votes: 0GitHub stars: 3
- Cv Photonic Qnn Edge MedicalParameter-efficient Continuous-Variable Photonic Quantum Neural Networks (CV-QNN) for edge medical AI applications. Covers room-temperature quantum computing on photonic hardware, MobileNet feature extraction + PCA dimensionality reduction, and CV-QNN classifiers for medical image classification (oral cancer, dermatology, radiology). Use when: (1) building edge-deployable quantum ML for healthcare, (2) comparing qubit vs CV photonic approaches, (3) designing parameter-efficient quantum classi...Votes: 0GitHub stars: 3
- Stablesleep Sourcefree Testtime Adaptation For Sleep Staging With Lightweight Safety Rails**arXiv ID:** 2509.02982 **Authors:** Hritik Arasu, Faisal R Jahangiri **Published:** 2025-09-03T03:42:31Z **Abstract:** Sleep staging models often degrade when deployed on patients with unseen physiology or recording conditions. We propose a streaming, source-free test-time adaptation (TTA) recipe that combines entropy minimization (Tent) with Batch-Norm statistic refresh and two safety rails: an entropy gate to pause adaptation on uncertain windows and an EMA-based reset to reel back drift....Votes: 0GitHub stars: 3
- Unifying Von Neumann Hpc Neuromorphic Ebbrains统一冯诺依曼HPC与神经形态计算的EBRAINS工作流框架 - 透明跨平台执行SNN，支持异构架构无缝切换。Votes: 0GitHub stars: 3
- Topological Fault Detection QuantumTopological Engine Monitor (TEM) methodology for non-invasive fault detection in quantum systems. Uses persistent homology and time-delay embeddings from weak measurements to diagnose control failures. Robust across diverse noise profiles. Activation: topological fault detection, quantum engine monitoring, persistent homology quantum, TEM monitoring.Votes: 0GitHub stars: 3
- Tensor Network Quantum FederatedPrivacy-aware federated learning combining tensor-network compression with quantum-enhanced processing for medical diagnosis. Use when building multi-institutional medical AI systems that need: (1) MPC-secured aggregation, (2) small-qubit quantum processing on compressed features, (3) tensor-network frontends (MPS/TTN/MERA). Addresses the dual challenge of communication overhead and qubit limitations.Votes: 0GitHub stars: 3
- Temporal Structural Fault DiscriminationDistributed fault discrimination in microservice architectures using joint temporal-structural representation learning. Models microservice operations as dynamic graph sequences, combines temporal GNN encoding with attention-based structured message passing, and uses dual readout for system-level fault classification. Use when: building distributed system fault detection, microservice anomaly detection, temporal graph neural networks for ops, service mesh observability, distributed fault diag...Votes: 0GitHub stars: 3
- Synpre Fl Synthetic Data Driven Pretraining IntegrSkill generated from arXiv paper 2607.19524: SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training frameworkVotes: 0GitHub stars: 3
- Surrogate Gradient Snn TrainingSurrogate Gradient Learning for Spiking Neural Networks - comprehensive training framework using differentiable surrogate functions to overcome the non-differentiability of spike functions. Includes multiple surrogate gradient types (fast-sigmoid, exponential, arctan, erf), temporal batch normalization, neuron normalization, and advanced training strategies for deep SNNs. Activation: surrogate gradient SNN, differentiable spike, spiking neural network training, SNN backpropagation, time surro...Votes: 0GitHub stars: 3
- Spikingnav Robust Embodied NavigationSpikingNav framework for robust embodied navigation using spiking neural networks. Implements Spiking Sensing Encoder (SSE) and Spiking Policy Network (SPN) for energy-efficient, robust indoor navigation on neuromorphic hardware. Use when working with embodied AI agents, robotics navigation, spiking neural networks for real-world applications, or neuromorphic computing deployment.Votes: 0GitHub stars: 3
- Spike Yolo Automotive PerceptionFirst comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking using SpikeYOLO transfer learning. Achieves mAP 0.937 (KITTI) and 0.771 (BDD100K MOT2020) for detection, HOTA 0.701/0.445 for tracking — competitive with conventional DL, with energy-efficient edge deployment. arXiv:2607.04921Votes: 0GitHub stars: 3
- Specbox Speculative Sandbox SchedulingRuntime framework for speculative sandbox preallocation and scheduling in LLM agent serving environments to optimize resource utilization and reduce tail latency.Votes: 0GitHub stars: 3
- Selectinfer Selective Neuron Loading And ComputatiDerived from arXiv:2607.18081 - SelectInfer: Selective Neuron Loading and Computation for On-Device LLMsVotes: 0GitHub stars: 3
- Reinformed Dreamer Asymmetric World ModelReinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.Votes: 0GitHub stars: 3
- Reasonstl Nl To StlTool-augmented process-rewarded learning for Natural Language to Signal Temporal Logic (STL) translation in cyber-physical systems (CPS) specification. Use when: (1) translating natural language CPS requirements into STL formulas, (2) building NL-to-formal-specification pipelines for autonomous systems, robotics, or CPS verification, (3) designing tool-augmented LLM frameworks for formal methods, (4) implementing process-rewarded training for structured output generation, (5) creating benchma...Votes: 0GitHub stars: 3
- Quiet Edge Centric Brain SynchronizationQUIET (Quantifying Underutilized Influential Edges for Targeted Synchronization) is an edge-centric framework that integrates structural controllability and functional connectivity to identify energy-efficient synchronization pathways in brain networks.Votes: 0GitHub stars: 3
- Quantum Safe Pqc DeploymentPost-quantum cryptography (PQC) production deployment methodology. Hybrid-by-default architecture bridging classical and post-quantum security for production systems. Covers ML-KEM/ML-DSA migration, TLS integration, and incremental deployment strategies.Votes: 0GitHub stars: 3
- Quantum Rl Dynamic PortfolioQuantum Reinforcement Learning (QRL) for dynamic portfolio optimization using Variational Quantum Circuits (VQC). Provides quantum analogues of Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) for sequential portfolio allocation. Achieves competitive performance vs classical deep RL with fewer trainable parameters. Use when: (1) implementing quantum RL for finance, (2) designing VQC-based trading agents, (3) comparing quantum vs classical RL for portfolio optimization, (4) b...Votes: 0GitHub stars: 3
- Quantum Resistant Network ArchitecturePost-quantum cryptography network architecture methodology covering PQC primitives, protocols (TLS, SSH), and best practices for network security migration. Addresses architectural consequences of post-quantum transition for networked systems including mobile networks, industrial IoT, and large-scale infrastructure. Use when designing post-quantum network security, migrating to PQC protocols, or evaluating quantum-resistant network architectures.Votes: 0GitHub stars: 3
- Quantum Picotesla Biomagnetism SensingPicotesla-scale magnetic environment design for quantum biomagnetism sensing. Enables optically pumped magnetometers, magnetocardiography (MCG), magnetoencephalography (MEG), and ultra-low field MRI without active dynamic compensation. Use when designing quantum sensing platforms for medical diagnostics, picotesla magnetic shielding, robotic magnetic field mapping, or biomagnetic measurement systems.Votes: 0GitHub stars: 3
- Quantum Optimization TransportationApply quantum optimization algorithms to transportation network problems including vehicle routing, urban logistics, and infrastructure planning. Covers compressed adiabatic evolution, constraint-preserving XY-mixers, QAOA variants, and hardware-efficient formulations. Use when formulating transportation combinatorial optimization as quantum problems, implementing constraint-preserving quantum mixers, or optimizing hardware resource usage for quantum annealing.Votes: 0GitHub stars: 3