Selected work

Representative Papers

Reducción de ruido por medio de autoencoders: caso de estudio con la señal GW150914

Oct 01, 2025

To address severe signal-to-noise ratio (SNR) degradation of low-amplitude astrophysical signals—such as gravitational waves—in the presence of strong, multi-source noise, this paper proposes an end-to-end nonlinear denoising method based on a deep autoencoder. The model jointly optimizes architecture and parameters using mixed data comprising real gravitational-wave events (e.g., GW150914) and synthetic noise, enabling adaptive extraction of weak signal features and effective noise suppression. Experiments demonstrate a substantial average SNR improvement of 38% over target signals, outperforming conventional filtering and shallow learning approaches. Crucially, this work provides the first systematic validation of autoencoders for denoising real astrophysical time-series data, establishing their effectiveness, robustness, and scalability. The method eliminates the need for prior noise modeling and is readily embeddable into real-time gravitational-wave analysis pipelines, offering a novel, data-driven paradigm for gravitational-wave detection.

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AI-Driven Generation of Old English: A Framework for Low-Resource Languages

Jul 26, 2025

The scarcity of Old English corpora severely limits its applicability in modern NLP. To address this, we propose a dual-agent generative framework that decouples content generation from stylistic transfer: one agent employs LoRA-efficient fine-tuning of large language models to generate authentic Old English exemplars; the other enhances linguistic fidelity via back-translation. Our approach integrates parameter-efficient fine-tuning, back-translation-based data augmentation, and automated evaluation (BLEU, METEOR, chrF), validated by linguistics experts. Experiments demonstrate a substantial improvement in Old English translation quality—BLEU scores rise significantly from 26 to over 65—while achieving high grammatical accuracy and stylistic consistency. The method effectively expands high-quality, reproducible Old English resources, establishing a novel paradigm for computational research on historical languages.

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Recent publications

Latest Papers

Reducción de ruido por medio de autoencoders: caso de estudio con la señal GW150914

Oct 01, 2025

To address severe signal-to-noise ratio (SNR) degradation of low-amplitude astrophysical signals—such as gravitational waves—in the presence of strong, multi-source noise, this paper proposes an end-to-end nonlinear denoising method based on a deep autoencoder. The model jointly optimizes architecture and parameters using mixed data comprising real gravitational-wave events (e.g., GW150914) and synthetic noise, enabling adaptive extraction of weak signal features and effective noise suppression. Experiments demonstrate a substantial average SNR improvement of 38% over target signals, outperforming conventional filtering and shallow learning approaches. Crucially, this work provides the first systematic validation of autoencoders for denoising real astrophysical time-series data, establishing their effectiveness, robustness, and scalability. The method eliminates the need for prior noise modeling and is readily embeddable into real-time gravitational-wave analysis pipelines, offering a novel, data-driven paradigm for gravitational-wave detection.

0 citationsRead paper

AI-Driven Generation of Old English: A Framework for Low-Resource Languages

Jul 26, 2025

The scarcity of Old English corpora severely limits its applicability in modern NLP. To address this, we propose a dual-agent generative framework that decouples content generation from stylistic transfer: one agent employs LoRA-efficient fine-tuning of large language models to generate authentic Old English exemplars; the other enhances linguistic fidelity via back-translation. Our approach integrates parameter-efficient fine-tuning, back-translation-based data augmentation, and automated evaluation (BLEU, METEOR, chrF), validated by linguistics experts. Experiments demonstrate a substantial improvement in Old English translation quality—BLEU scores rise significantly from 26 to over 65—while achieving high grammatical accuracy and stylistic consistency. The method effectively expands high-quality, reproducible Old English resources, establishing a novel paradigm for computational research on historical languages.

0 citationsRead paper