1941 papers
The spectrum from Cs to Sy captures a fascinating range of scientific inquiry, bridging the gap between complex theoretical models and tangible real-world applications. These studies often explore intricate systems where small changes can lead to significant outcomes, offering fresh perspectives on how we understand the natural world. Whether investigating chemical reactions or studying biological systems, this collection highlights the dynamic interplay between different scientific disciplines.
At Gist.Science, we monitor arXiv daily to ensure you never miss a breakthrough in this evolving field. For every new preprint that appears in the Cs — Sy category, our team processes the findings to generate both a clear, plain-language overview and a detailed technical summary. This dual approach ensures that insights are accessible to everyone, from curious beginners to seasoned experts.
Below are the latest preprints from arXiv in this category, complete with our summaries to help you navigate the research.
This paper proposes Online-Score-Aided Federated Learning (OSAFL), a novel algorithm designed to address the challenges of resource-constrained wireless clients with limited storage and continual data arrival by theoretically analyzing convergence bounds under various constraints and optimizing global aggregation weights to minimize errors.
This paper demonstrates that the -optimal feedback control problem for passive plants with output-strictly passive constraints can be reformulated as a convex, infinite-dimensional optimization over the Youla parameter, which is effectively approximated by converging finite-dimensional truncations to yield sub-optimal controllers and lower bounds.
This paper introduces a Generalist Controller, a single-shot learning-based algorithm that uses attention mechanisms and a mixture-of-experts architecture to effectively control diverse single-input single-output systems of varying orders and dynamics without requiring system-specific tuning or architectural changes.
This paper introduces ffconnect, an open-source Python interface for the mid-fidelity FAST.Farm simulator that streamlines wind farm control integration with modern machine learning ecosystems through a restructured API while maintaining negligible runtime overhead.
This paper addresses the challenges of controlling agile fish-like robots by introducing a computationally efficient simulation platform and a differentiable reinforcement learning approach that optimizes PID gains through backpropagation and curriculum training, successfully transferring the learned policy from simulation to a physical robot for accurate path tracking.
This paper proposes a fully distributed adaptive estimation scheme for discrete-time nonlinear systems with unknown source dynamics over directed networks, which utilizes only local measurements and neighbor exchanges to achieve robust state estimation without requiring shared inputs, while establishing theoretical stability guarantees and demonstrating scalability through numerical simulations.
This paper argues that while batteries are crucial for Great Britain's decarbonization and net zero goals, the full potential of electric vehicle batteries as a storage resource will only be realized through coordinated dispatch, improved market design, and better system integration to prevent unmanaged charging from creating grid challenges.
This paper presents a comparative dynamic analysis showing that while hydrogen networks exhibit faster transient recovery after compressor failures than natural gas networks, their operational flexibility and demand curtailment levels are critically dependent on pipe diameter selection, which significantly influences pressure losses.
This paper proposes a distributionally robust covariance-steering framework that utilizes relative entropy constraints and computable upper bounds on risk-sensitive quantities to design stochastic guidance policies for nonlinear systems, ensuring the true state distribution remains close to a Gaussian surrogate while mitigating estimation errors caused by distribution ambiguity.
This paper proposes a joint optimization framework for transmit coefficients and receive combining vectors in over-the-air federated learning under imperfect channel state information, utilizing Lyapunov-based optimization to minimize long-term mean squared error and preserve model accuracy across multiple datasets.