Publications associées à A Generalization of the Finite-Length Scaling Approach Beyond the BEC

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Shafranov shift correction to the Furth-Yoshikawa scaling of tokamak adiabatic compression

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Communication-efficient distributed training of machine learning models

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Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

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In 1948, Claude Shannon laid the foundations of information theory, which grew out of a study to find the ultimate limits of source compression, and of reliable communication. Since then, information theory has proved itself not only as a quest to find the ...

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Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. N ...

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Large-scale traffic signal control and multimodal network design

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A Scaling Law for SPAD Pixel Miniaturization

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The growing demands on compact and high-definition single-photon avalanche diode (SPAD) arrays have motivated researchers to explore pixel miniaturization techniques to achieve sub-10 μm pixels. The scaling of the SPAD pixel size has an impact on key perfo ...

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Can we use machine learning to compress graph data? The absence of ordering in graphs poses a significant challenge to conventional compression algorithms, limiting their attainable gains as well as their ability to discover relevant patterns. On the other ...

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Theory-based scaling laws of near and far scrape-off layer widths in single-null L-mode discharges

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Theory-based scaling laws of the near and far scrape-off layer (SOL) widths are analytically derived for L-mode diverted tokamak discharges by using a two-fluid model. The near SOL pressure and density decay lengths are obtained by leveraging a balance amo ...

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