Change-point analysis: a new perspective for unstable financial markets
本文针对金融市场的不稳定性，提出基于修正Hankel变换和Laplace变换的两类新的非参数变点检测方法，并通过模拟和实际数据验证了其有效性。
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Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
This study addresses the challenge of safely and effectively reducing sample sizes in registered clinical trials while adhering to regulatory requirements. Building upon the FDA’s seven-step risk assessment framework, the work presents the first systematic application of AI model trustworthiness evaluation guidelines to the context of sample size reduction. By constructing prognostic covariates, conducting risk-informed model development and validation, and integrating these with statistical re-estimation methods, the approach recalculates the required trial sample size. Demonstrated in a randomized controlled trial for Alzheimer’s disease, the methodology enabled prospective sample size reduction, substantially shortening trial duration, lowering costs, and accelerating the availability of effective therapies. This provides a generalizable, AI-driven framework for enhancing the efficiency of drug development.
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Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
This study addresses a critical limitation in existing retrieval-augmented generation (RAG) benchmarks: their inability to disentangle performance gains stemming from improvements in retrieval mechanisms versus those arising from enhanced document representations. To isolate the impact of document preprocessing, the authors fix the retriever—using BM25 as a consistent baseline—and systematically evaluate diverse document transcription and preprocessing strategies across multilingual and visually dense RAG tasks. Their experiments reveal that optimizing document representation alone substantially narrows the performance gap between BM25 and state-of-the-art multimodal retrievers, indicating that much of the observed gain in current systems originates from representation quality rather than retrieval algorithmic advances. Based on these findings, the work advocates for a new benchmarking paradigm that decouples document transcription from retrieval capability to enable more precise evaluation of RAG components.
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Change-point analysis: a new perspective for unstable financial markets
本文针对金融市场的不稳定性，提出基于修正Hankel变换和Laplace变换的两类新的非参数变点检测方法，并通过模拟和实际数据验证了其有效性。
0 citationsRead paper
Regulatory Considerations for Using Artificial Intelligence Models to Reduce Sample Sizes in Registrational Studies
This study addresses the challenge of safely and effectively reducing sample sizes in registered clinical trials while adhering to regulatory requirements. Building upon the FDA’s seven-step risk assessment framework, the work presents the first systematic application of AI model trustworthiness evaluation guidelines to the context of sample size reduction. By constructing prognostic covariates, conducting risk-informed model development and validation, and integrating these with statistical re-estimation methods, the approach recalculates the required trial sample size. Demonstrated in a randomized controlled trial for Alzheimer’s disease, the methodology enabled prospective sample size reduction, substantially shortening trial duration, lowering costs, and accelerating the availability of effective therapies. This provides a generalizable, AI-driven framework for enhancing the efficiency of drug development.
0 citationsRead paper
Retrieval or Representation? Reassessing Benchmark Gaps in Multilingual and Visually Rich RAG
This study addresses a critical limitation in existing retrieval-augmented generation (RAG) benchmarks: their inability to disentangle performance gains stemming from improvements in retrieval mechanisms versus those arising from enhanced document representations. To isolate the impact of document preprocessing, the authors fix the retriever—using BM25 as a consistent baseline—and systematically evaluate diverse document transcription and preprocessing strategies across multilingual and visually dense RAG tasks. Their experiments reveal that optimizing document representation alone substantially narrows the performance gap between BM25 and state-of-the-art multimodal retrievers, indicating that much of the observed gain in current systems originates from representation quality rather than retrieval algorithmic advances. Based on these findings, the work advocates for a new benchmarking paradigm that decouples document transcription from retrieval capability to enable more precise evaluation of RAG components.
0 citationsRead paper