1683 papers
The category labeled "Eess — Sy" represents the dynamic intersection of electrical engineering and systems science. This field explores how complex networks function, from the power grids that light our cities to the algorithms that manage traffic flow and industrial automation. It is where engineers design the intelligent frameworks that keep modern society running smoothly and efficiently.
Gist.Science curates the latest research in this domain directly from arXiv, ensuring you never miss a breakthrough. Our team processes every new preprint in this category as soon as it appears, offering both accessible plain-language explanations and detailed technical summaries to help you grasp the core innovations without getting lost in the math.
Below are the most recent papers in electrical engineering and systems science, ready for you to explore and understand.
This experimental study demonstrates that the karma mechanism achieves near-Pareto improvements over random allocation in dynamic resource settings, even when human subjects deviate significantly from theoretically optimal bidding strategies, thereby establishing behaviorally robust performance bounds for real-world implementation.
This paper proposes an Experience Feedback Graph Diffusion (EFGD) algorithm to solve the NP-hard problem of robust sensor placement in interdependent Cyber-Physical Power Systems, simultaneously optimizing physical anomaly detection and cyber communication resilience while demonstrating superior convergence and reward performance over existing diffusion-based methods.
This paper proposes a unified framework combining the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to bridge the gap between geometric control and semantic reasoning in autonomous robotics by preserving topological relational integrity and ensuring delay-robust, cognitively transparent decision-making.
OmniRetarget is an interaction-preserving data generation engine that leverages an interaction mesh to overcome embodiment gaps and preserve critical human-object/environment relationships, enabling the efficient creation of high-quality training data for robust, long-horizon humanoid loco-manipulation and parkour skills without complex learning curricula.
This paper introduces the Fair Play Automatic Market Maker (FP-AMM), a stateful stochastic allocation mechanism that overcomes the memoryless limitations of traditional pricing by employing a two-stage clearing rule and shortage-memory tracking to mathematically guarantee equitable electricity delivery and maintain grid feasibility during scarcity.
This paper introduces SkillChain-Gym, a novel benchmark for reskilling-aware production-inventory control that models dynamic workforce capabilities and disruptions, demonstrating through extensive evaluation that no single policy dominates across all regimes, thereby highlighting the need for forecast-driven strategies that balance proactive skill insurance with reactive adaptation.
This paper proposes a skill-constrained model predictive control framework for resilient manufacturing supply chains and demonstrates through extensive synthetic testing that its effectiveness is regime-dependent, offering advantages only when skill bottlenecks are forecastable early enough for training, while static insurance plans remain superior under surprise shocks or tight capacity constraints.
This paper proposes a performance-driven environment abstraction framework for large Markov decision processes that utilizes a multi-timescale reinforcement learning algorithm to dynamically refine tree-structured state partitions based on Q-value discrepancies, thereby optimizing decision quality while balancing sample efficiency and computational complexity.
This paper introduces Perron--Frobenius Operator Matching (PFOM), a unified generative framework that subsumes flow, diffusion, and jump models by matching density evolution via the integral PF operator, while establishing Kullback--Leibler divergence as the unique Bregman loss for practical training and leveraging Nesterov acceleration to improve convergence and sampling efficiency.
The paper proposes OmniDroneX, a unified Drone-as-a-Service ecosystem that leverages large language models and a formal abstraction model to transform fixed-function drones into dynamically composable, self-evolving entities capable of executing complex missions through natural language interaction and multi-layered service composition.