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Deployment, cloud, containers, CI/CD, monitoring, and infrastructure
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- Deploymentaligned Lowprecision Neural Architecture Search For Spaceborne Edge Ai**arXiv ID:** 2604.24492 **Authors:** Parampuneet Kaur Thind, Vaibhav Katturu, Giacomo Zema, Roberto Del Prete **Published:** 2026-04-27T13:58:18Z **Abstract:** Designing deep networks that meet strict latency and accuracy constraints on edge accelerators increasingly relies on hardware-aware optimization, including neural architecture search (NAS) guided by device-level metrics. Yet most hardware-aware NAS pipelines still optimize architectures under full-precision assumptions and apply low-...Votes: 0GitHub stars: 3
- A Hybrid Swarm Intelligence Approach For Optimizing Multimodal Large Language Models Deployment In Edgecloudbased Federated Learning Environments**arXiv ID:** 2502.10419 **Authors:** Gaith Rjouba, Hanae Elmekki, Saidul Islam, Jamal Bentahar, Rachida Dssouli **Published:** 2025-02-04T03:03:24Z **Abstract:** The combination of Federated Learning (FL), Multimodal Large Language Models (MLLMs), and edge-cloud computing enables distributed and real-time data processing while preserving privacy across edge devices and cloud infrastructure. However, the deployment of MLLMs in FL environments with resource-constrained edge devices presents si...Votes: 0GitHub stars: 3
- Defensibility Analysis Shield SynthesisNetwork defensibility analysis using shield synthesis and adversarial game theory. Reinterprets shielded RL from runtime enforcement to design-time structural analysis. Use when: analyzing network topology security, synthesizing safety shields for RL agents, computing defensibility verdicts for cyber-physical systems, designing secure multi-agent architectures, evaluating network architecture defensibility, or combining formal verification with adversarial RL.Votes: 0GitHub stars: 3
- Systems Engineering Research Aug2026August 2026 systems engineering research synthesis.Votes: 0GitHub stars: 3
- Latent Rag ContinuousShift RAG reasoning and retrieval from discrete language to continuous latent space for ~90% latency reduction. Based on arXiv 2605.06285.Votes: 0GitHub stars: 3
- Data Center Ai Workload PowerData center AI workload power profiling and infrastructure planning. Methods for measuring generative AI workload power consumption at high resolution, scaling to whole-facility energy demand, and planning infrastructure for grid connection, microgrids, and on-site generation. Triggers: data center power, AI energy consumption, GPU power profiling, facility infrastructure planning, generative AI workload, H100 power measurement, MLCommons benchmark power.Votes: 0GitHub stars: 3
- Dance Eeg Event DetectionDANCE (Detect And Classify Events) — deep learning pipeline for joint event detection and classification from continuous, unaligned EEG signals. Frames neural decoding as a set-prediction problem, eliminating the need for pre-aligned event windows. Achieves SOTA on seizure monitoring and matches onset-informed BCI models. Use when working with: EEG event detection, continuous neural decoding, seizure detection, asynchronous BCI, set prediction for time series, or real-time neural monitoring. ...Votes: 0GitHub stars: 3
- Dance Eeg Event Detection ClassificationDANCE (Detect and Classify Events in EEG) — deep learning pipeline that frames neural decoding as a set-prediction problem for joint event detection and classification from raw, unaligned EEG signals. Activation triggers: DANCE, EEG event detection, set prediction, asynchronous neural decoding, event-based EEG, raw EEG decoding, event classification, Meta AI EEG.Votes: 0GitHub stars: 3
- Counterfactual Brain DynamicsCounterfausal causal analysis framework for brain network dynamics using Hodge theory and minimum-energy principles. Models pathological disruptions and therapeutic interventions as energy-perturbation problems on network flows. Decomposes directed communication into dissipative and persistent (harmonic) components. Use when analyzing brain network causality, counterfactual interventions, network resilience, epilepsy models, Hodge decomposition on brain graphs, or causal inference beyond Gran...Votes: 0GitHub stars: 3
- Convolution Delay Learning SnnCombines convolutional recurrent connections with DelRec delay learning mechanism in recurrent spiking neural networks. Achieves 99% parameter savings and 52x faster inference vs standard recurrent SNN while maintaining accuracy on audio classification. Convolutional recurrent structure with learned axonal delays provides streamlined architecture for resource-constrained edge systems. Activation: convolution delay learning, DelRec, recurrent SNN audio, axonal delay learning, efficient SNN arc...Votes: 0GitHub stars: 3
- State Adaptive Error CorrectionState-adaptive error correction and fault tolerance methodology. Use when designing resilient systems that need to adapt error handling based on current system state, optimize recovery strategies dynamically, or build fault-tolerant architectures. Applicable to distributed systems, quantum computing, network protocols, ML pipelines, and control systems. Trigger words: state-adaptive, adaptive error correction, fault-tolerant design, resilient architecture, dynamic error handling, system state...Votes: 0GitHub stars: 3
- Koopman Stability Preserving IdStability-preserving system identification using Koopman operator lifting with ISS-LMI constraints. Enables data-driven modeling of nonlinear systems (especially Persidskii-class and electromechanical systems) while guaranteeing input-to-state stability. Use when: (1) identifying nonlinear system models from trajectory data, (2) needing stability guarantees in learned models, (3) working with Persidskii systems or sector-bounded nonlinearities, (4) designing robust observers for partially-mea...Votes: 0GitHub stars: 3
- Context Reconfiguration Sparse TemporalMechanistic analysis of joint sparse coding and temporal dynamics as the neural basis for context reconfiguration. Combines mouse mPFC recordings with computational network analysis to show how sparsity reduces cross-context interference while temporal dynamics enhance context separability. Establishes SNNs as naturally endowed with both properties, enabling lifelong learning retention without auxiliary heuristics. Energy-efficient architectural principle for stable adaptation. Activation tri...Votes: 0GitHub stars: 3
- Complex System Robustness CollapseComplex system robustness and collapse analysis - temporal structure, percolation methods, phase transitions, bistability, catastrophic collapse. Activation: system robustness, system collapse, complex network, phase transition, resilience analysis.Votes: 0GitHub stars: 3
- Competitive Complementary ToolsMethodology for modeling the co-evolution of human competence and AI tool reliance as a bistable dynamical system. Analyzes competence collapse thresholds, transparency effects, and agency transfer in human-AI collaboration. Use when designing AI tools, studying human-AI interaction dynamics, or developing tool-resistant education strategies.Votes: 0GitHub stars: 3
- Cold Atom Medical ImagingMedical imaging classification using cold-atom (neutral-atom) reservoir computing with auto-encoders and surrogate-driven training. Based on arXiv:2605 (2026). Use when building hybrid quantum-classical pipelines for medical image analysis, applying neutral-atom reservoir computing to classification tasks, or dimensionality reduction for medical imaging. Combines guided auto-encoder for feature compression with physical reservoir dynamics for classification. Activation: cold-atom reservoir co...Votes: 0GitHub stars: 3
- Cluster Based Distributed Stability CertificatesCluster-based distributed small-signal stability certification for grid-forming inverter networks. Use when: (1) certifying stability of large-scale inverter networks without a fully assembled global model; (2) designing cluster-based or decentralized stability certificates for grid-forming inverters; (3) analyzing voltage and angle-frequency subsystem stability via small-gain and energy arguments; (4) selecting network partitioning resolution to match operational boundaries. Activation: grid...Votes: 0GitHub stars: 3
- Clp Snn Loihi2 Continual LearningOnline Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network (CLP-SNN). Covers self-normalizing local learning rules, spike-driven neural state machines for autonomous on-chip learning, and breakthrough efficiency gains on neuromorphic hardware. Achieves 113x lower latency and 6,600x lower energy than edge-GPU baselines while matching replay-based accuracy rehearsal-free. Use when: implementing continual learning on neuromorphic hardware, designing SNNs for edge AI depl...Votes: 0GitHub stars: 3
- Cloud Continuum Cps InfrastructureTwo-level reference architecture for Cloud Continuum experimentation separating research-infrastructure layer from application layer with Edge-Fog-Cloud patterns.Votes: 0GitHub stars: 3
- Clad Federated Anomaly DetectionClustered Label-Agnostic Federated Learning (CLAD) framework for anomaly detection in distributed systems. Combines unsupervised clustering with supervised detection via DM²A (Dual-Mode Multi-Stage Aggregation), enabling privacy-preserving anomaly detection across heterogeneous federated clients without shared labels.Votes: 0GitHub stars: 3
- Chia Agentic Hardware Software CodesignCHIA framework for principled agentic AI-driven hardware/software co-design. Treat hardware/software design flows as directed cyclic graphs (CHIA loops) with nodes executing SoC design tools, simulators, AI models, and evolutionary agents. Supports Chipyard, gem5, ChampSim, FireSim, Vivado, AlphaEvolve, and more. Enables isolation, profiling, fault-tolerant execution, and reliability across heterogeneous systems.Votes: 0GitHub stars: 3
- Cd Qaoa Peptide Structure PredictionCounter-Diabatic QAOA (CD-QAOA) methodology for peptide structure prediction on tetrahedral lattices. Accelerates convergence via counter-diabatic driving terms, validated against HF/DFT/MD/H-REMD. Use when: (1) quantum optimization for molecular/peptide structure prediction, (2) CD-QAOA for ground-state search acceleration, (3) quantum-classical hybrid validation of predicted structures, (4) Miyazawa-Jernigan interaction modeling for peptides. Activation: CD-QAOA, peptide structure predictio...Votes: 0GitHub stars: 3
- Canonical Functionalism ConsciousnessCanonical Functionalism — a mathematical refinement of computational functionalism about consciousness that identifies consciousness-relevant functional organization with a system's canonical functional structure: the minimal state-transition structure obtained by identifying internal states with identical future behavior under all possible continuations. Activation: canonical functionalism, consciousness theory, computational functionalism, observer-relative computation, consciousness invari...Votes: 0GitHub stars: 3
- Brainfuse Unified Biological Ai InfrastructureBrainFuse - unified infrastructure integrating realistic biological neural modeling and core AI methodology. Supports differentiable biophysical neuron simulation, 3000x GPU acceleration for ion-channel dynamics, and neuromorphic hardware deployment.Votes: 0GitHub stars: 3