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Doc-to-Atom — Learning to Compile and Compose Memory Atoms

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Doc-to-Atom — Learning to Compile and Compose Memory Atoms

·8 min read·Xingjian Diao, Wenbo Li, Yashas Malur Saidutta et al.

Doc-to-Atom (Doc2Atom) addresses the inefficiencies and limitations of prior document internalization approaches like Doc-to-LoRA, which compress an entire document into a single monolithic low-ran…

researchlarge-language-modelsparametric-memorycontext-distillationlow-rank-adapters

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Getting to know the Stellar Clusters in NGC 1569 — Bayesian inference of stellar cluster properties in a dwarf starburst galaxy

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Getting to know the Stellar Clusters in NGC 1569 — Bayesian inference of stellar cluster properties in a dwarf starburst galaxy

·10 min read·Bjarki Björgvinsson, Anna F. McLeod, Bronwyn Reichardt Chu et al.

This paper addresses the challenge of accurately inferring the physical properties—age and mass—of star clusters in the dwarf starburst galaxy NGC 1569, which is located about 3.25 Mpc away and und…

researchbayesian-inferencestochastic-modelingstar-clustersdwarf-galaxies

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MATLAB-Based Layerwise Self-Adaptive Physics-Informed Neural Network in Applications to Multidimensional Coupled Burgers' Equations with High Reynolds Numbers

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MATLAB-Based Layerwise Self-Adaptive Physics-Informed Neural Network in Applications to Multidimensional Coupled Burgers' Equations with High Reynolds Numbers

·9 min read·Harish P. Bhatt, Xi Chen, Jingsai Liang

This paper addresses the challenge of accurately simulating multidimensional coupled Burgers' equations (MCBEs) at high Reynolds numbers, where sharp shock fronts and steep gradients develop over time

researchphysics-informed-neural-networksadaptive-loss-weightingburgers-equationhigh-reynolds-number

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