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pro vyhledávání: '"Yerin A"'
Due to the subjective nature of current clinical evaluation, the need for automatic severity evaluation in dysarthric speech has emerged. DNN models outperform ML models but lack user-friendly explainability. ML models offer explainable results at a
Externí odkaz:
http://arxiv.org/abs/2412.03784
Autor:
Wu, Yu, Huang, Daiqiang, Zhang, Huanyu, Guarino, Anita, Fittipaldi, Rosalba, Ma, Chao, Hu, Wenjie, Chang, Niu, Wang, Zhen, Yu, Weichao, Yerin, Yuriy, Vecchione, Antonio, Liu, Yang, Cuoco, Mario, Guo, Hangwen, Shen, Jian
The relation between superconductivity and time-reversal symmetry (TRS) is one of the most fascinating problems in condensed matter physics. Although most superconductors inherently possess TRS, nonmagnetic disorder can induce states that demonstrate
Externí odkaz:
http://arxiv.org/abs/2411.01232
In line with the principle of honesty, there has been a growing effort to train large language models (LLMs) to generate outputs containing epistemic markers. However, evaluation in the presence of epistemic markers has been largely overlooked, raisi
Externí odkaz:
http://arxiv.org/abs/2410.20774
Despite the success of Large Language Models (LLMs), they still face challenges related to high inference costs and memory requirements. To address these issues, Knowledge Distillation (KD) has emerged as a popular method for model compression, with
Externí odkaz:
http://arxiv.org/abs/2410.19503
The Seebeck effect consists in the induction of a voltage drop due to the temperature difference in a conductor. In the middle of XIXth century, Lord Kelvin has proposed a relation between the Seebeck coefficient and the derivative of the chemical po
Externí odkaz:
http://arxiv.org/abs/2405.10807
Publikováno v:
Phys. Rev. B 110, 054501 (2024)
We consider nonreciprocal supercurrent effects in Josephson junctions based on multiband superconductors with a pairing structure that can break time-reversal symmetry. We demonstrate that a nonreciprocal supercurrent can be generally achieved by the
Externí odkaz:
http://arxiv.org/abs/2404.12641
Despite advancements in on-topic dialogue systems, effectively managing topic shifts within dialogues remains a persistent challenge, largely attributed to the limited availability of training datasets. To address this issue, we propose Multi-Passage
Externí odkaz:
http://arxiv.org/abs/2403.05814
Dysarthria, a common issue among stroke patients, severely impacts speech intelligibility. Inappropriate pauses are crucial indicators in severity assessment and speech-language therapy. We propose to extend a large-scale speech recognition model for
Externí odkaz:
http://arxiv.org/abs/2402.18923
Well-known Mott's formula links the thermoelectric power characterised by Seebeck coefficient to conductivity. We calculate analytically the thermoelectric current and Seebeck coefficient in one-dimensional systems and show that, while the prediction
Externí odkaz:
http://arxiv.org/abs/2401.03721
To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed. However, the original dialog inpainting model is trained solely on
Externí odkaz:
http://arxiv.org/abs/2311.07589