Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
The annals of mathematical statistics , pages=
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Lamarckian inheritance improves evolutionary robotics performance in dynamic environments unless changes are conflicting and unpredictable; a change-detecting sensor restores benefits.
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
citing papers explorer
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Identifying the nonlinear string dynamics with port-Hamiltonian neural networks
Port-Hamiltonian neural networks extended to PDEs recover the Hamiltonian and dissipation of nonlinear string dynamics from data and outperform non-physics-informed baselines.
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Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics
Lamarckian inheritance improves evolutionary robotics performance in dynamic environments unless changes are conflicting and unpredictable; a change-detecting sensor restores benefits.
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BLOOM is a 176B-parameter open-access multilingual language model trained on the ROOTS corpus that achieves competitive performance on benchmarks, with improved results after multitask prompted finetuning.
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The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance
LLM-generated feedback was associated with faster time to solution for programming students than compiler messages alone, with less-guided versions showing slightly stronger effects.
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