243,036 research outputs found

    MicroConceptBERT: concept-relation based document information extraction framework.

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    Extracting information from documents is a crucial task in natural language processing research. Existing information extraction methodologies often focus on specific domains, such as medicine, education or finance, and are limited by language constraints. However, more comprehensive approaches that transcend document types, languages, contexts, and structures would significantly advance the field proposed in recent research. This study addresses this challenge by introducing microConceptBERT: a concept-relations-based framework for document information extraction, which offers flexibility for various document processing tasks while accounting for hierarchical, semantic, and heuristic features. The proposed framework has been applied to a question-answering task on benchmark datasets: SQUAD 2.0 and DOCVQA. Notably, the F1 evaluation metric attains an outperforming 87.01 performance rate on the SQUAD 2.0 dataset compared to baseline models: BERT-base and BERT-large models

    Simultaneous detection of the nonlinear restoring and excitation of a forced nonlinear oscillation: an integral approach

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    We address in this article, how to calculate the restoring characteristic and the excitation of a nonlinear forced oscillating system. Under the assumption that the forced nonlinear oscillator has a periodic solution with period, we constructed a system of linear equations by introducing time-dependent multipliers. The periodicity assumption helps simplify the system of linear equations. The stability and uniqueness are also presented for the inverse problem. Numerical testing is conducted to show the effectiveness of our presented methodology.Peer ReviewedPostprint (author's final draft
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