Výsledky vyhledávání - "García, Sofía"

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Connected Speech-Based Cognitive Assessment in Chinese and English

Autor: Luz, Saturnino, Garcia, Sofia De La Fuente, Haider, Fasih, Fromm, Davida, MacWhinney, Brian, Lanzi, Alyssa, Chang, Ya-Ning, Chou, Chia-Ju, Liu, Yi-Chien

We present a novel benchmark dataset and prediction tasks for investigating approaches to assess cognitive function through analysis of connected speech. The dataset consists of speech samples and clinical information for speakers of Mandarin Chinese

Externí odkaz: http://arxiv.org/abs/2406.10272

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A Frame-based Attention Interpretation Method for Relevant Acoustic Feature Extraction in Long Speech Depression Detection

Autor: Deng, Qingkun, Luz, Saturnino, Garcia, Sofia de la Fuente

Speech-based depression detection tools could help early screening of depression. Here, we address two issues that may hinder the clinical practicality of such tools: segment-level labelling noise and a lack of model interpretability. We propose a sp

Externí odkaz: http://arxiv.org/abs/2406.03138

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Coded Computing Meets Quantum Circuit Simulation: Coded Parallel Tensor Network Contraction Algorithm

Autor: Lee, Jin, Gonzalez-Garcia, Sofia, Zhang, Zheng, Jeong, Haewon

Parallel tensor network contraction algorithms have emerged as the pivotal benchmarks for assessing the classical limits of computation, exemplified by Google's demonstration of quantum supremacy through random circuit sampling. However, the massive

Externí odkaz: http://arxiv.org/abs/2405.13946

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Hierarchical attention interpretation: an interpretable speech-level transformer for bi-modal depression detection

Autor: Deng, Qingkun, Luz, Saturnino, Garcia, Sofia de la Fuente

Depression is a common mental disorder. Automatic depression detection tools using speech, enabled by machine learning, help early screening of depression. This paper addresses two limitations that may hinder the clinical implementations of such tool

Externí odkaz: http://arxiv.org/abs/2309.13476

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Random insights into the complexity of two-dimensional tensor network calculations

Autor: Gonzalez-Garcia, Sofia, Sang, Shengqi, Hsieh, Timothy H., Boixo, Sergio, Vidal, Guifre, Potter, Andrew C., Vasseur, Romain

Publikováno v: Phys. Rev. B 109, 235102 (2024)

Projected entangled pair states (PEPS) offer memory-efficient representations of some quantum many-body states that obey an entanglement area law, and are the basis for classical simulations of ground states in two-dimensional (2d) condensed matter s

Externí odkaz: http://arxiv.org/abs/2307.11053

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