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Sandra Gomez

Publications and source records attributed to Sandra Gomez.

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Attochemical control of nuclear motion despite fast electronic decoherence

Short-in-time, broad-in-energy attosecond or few-femtosecond pulses can excite coherent superpositions of several electronic states in molecules. This results in ultrafast charge oscillations known as charge migration. A key open question in the emerging field of attochemistry is whether these electron dynamics, which due to decoherence often last only for a few femtoseconds, can influence longer-time scale nuclear rearrangements. Herein, we address this question through full-dimensional quantum dynamics simulations of the coupled electron-nuclear dynamics initiated by ionization and coherent excitation of ethylene. The simulations of this prototype organic chromophore predict electronic coherences with half-lives of less than 1 fs. Despite their brevity, these electronic coherences induce vibrational coherences along the derivative coupling vectors that persist for at least 50 fs. These results suggest that short-lived electronic coherences can impart long-lasting legacies on nuclear motion, a finding of potential importance to the interpretation of attosecond experiments and the development of strategies for attochemical control.

physics.chem-ph

Automatic Speech Recognition Advancements for Indigenous Languages of the Americas

Indigenous languages are a fundamental legacy in the development of human communication, embodying the unique identity and culture of local communities in America. The Second AmericasNLP (Americas Natural Language Processing) Competition Track 1 of NeurIPS (Neural Information Processing Systems) 2022 proposed the task of training automatic speech recognition (ASR) systems for five Indigenous languages: Quechua, Guarani, Bribri, Kotiria, and Wa'ikhana. In this paper, we describe the fine-tuning of a state-of-the-art ASR model for each target language, using approximately 36.65 h of transcribed speech data from diverse sources enriched with data augmentation methods. We systematically investigate, using a Bayesian search, the impact of the different hyperparameters on the Wav2vec2.0 XLS-R (Cross-Lingual Speech Representations) variants of 300 M and 1 B parameters. Our findings indicate that data and detailed hyperparameter tuning significantly affect ASR accuracy, but language complexity determines the final result. The Quechua model achieved the lowest character error rate (CER) (12.14), while the Kotiria model, despite having the most extensive dataset during the fine-tuning phase, showed the highest CER (36.59). Conversely, with the smallest dataset, the Guarani model achieved a CER of 15.59, while Bribri and Wa'ikhana obtained, respectively, CERs of 34.70 and 35.23. Additionally, Sobol' sensitivity analysis highlighted the crucial roles of freeze fine-tuning updates and dropout rates. We release our best models for each language, marking the first open ASR models for Wa'ikhana and Kotiria. This work opens avenues for future research to advance ASR techniques in preserving minority Indigenous languages

cs.CL