Zobrazeno 1 - 10
of 65 574
pro vyhledávání: '"A. Richmond"'
Autor:
J. Yang, K. Bergdorf, C. Yan, W. Luo, S. C. Chen, G.D. Ayers, Q. Liu, X. Liu, M. Boothby, V.L. Weiss, S. M. Groves, A. N. Oleskie, X. Zhang, D. Y. Maeda, J. A. Zebala, V. Quaranta, A. Richmond
Publikováno v:
Molecular Cancer, Vol 22, Iss 1, Pp 1-17 (2023)
Abstract Background Though the CXCR2 chemokine receptor is known to play a key role in cancer growth and response to therapy, a direct link between expression of CXCR2 in tumor progenitor cells during induction of tumorigenesis has not been establish
Externí odkaz:
https://doaj.org/article/275116f428d144b2a7d5b57814c8b793
Autor:
Mao, Haiyi, Lopez, Romain, Liu, Kai, Huetter, Jan-Christian, Richmond, David, Benos, Panayiotis, Qiu, Lin
The study of cells and their responses to genetic or chemical perturbations promises to accelerate the discovery of therapeutic targets. However, designing adequate and insightful models for such data is difficult because the response of a cell to pe
Externí odkaz:
http://arxiv.org/abs/2410.22472
Autor:
Yang, Qian, Wong, Richmond Y, Jackson, Steven J, Junginger, Sabine, Hagan, Margaret D, Gilbert, Thomas, Zimmerman, John
Policies significantly shape computation's societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at
Externí odkaz:
http://arxiv.org/abs/2409.19738
Emotion recognition from speech and music shares similarities due to their acoustic overlap, which has led to interest in transferring knowledge between these domains. However, the shared acoustic cues between speech and music, particularly those enc
Externí odkaz:
http://arxiv.org/abs/2409.17899
Utilizing Self-Supervised Learning (SSL) models for Speech Emotion Recognition (SER) has proven effective, yet limited research has explored cross-lingual scenarios. This study presents a comparative analysis between human performance and SSL models,
Externí odkaz:
http://arxiv.org/abs/2409.16920
Autor:
Sun, Siqi, Richmond, Korin
Recent work has shown the feasibility and benefit of bootstrapping an integrated sequence-to-sequence (Seq2Seq) linguistic frontend from a traditional pipeline-based frontend for text-to-speech (TTS). To overcome the fixed lexical coverage of bootstr
Externí odkaz:
http://arxiv.org/abs/2409.09891
While recent Zero-Shot Text-to-Speech (ZS-TTS) models have achieved high naturalness and speaker similarity, they fall short in accent fidelity and control. To address this issue, we propose zero-shot accent generation that unifies Foreign Accent Con
Externí odkaz:
http://arxiv.org/abs/2409.09098
Autor:
Oh, Suho, Richmond, Edward
In this chapter, we give an overview of Billey-Postnikov (BP) decompositions which have become an important tool for understanding the geometry and combinatorics of Schubert varieties. BP decompositions are factorizations of Coxeter group elements wi
Externí odkaz:
http://arxiv.org/abs/2409.03096
Autor:
Park, Jinseo, Angelico, Evan, Arzac, Andrew, Braga, Davide, Datta, Ahan, England, Troy, Ertley, Camden, Fahim, Farah, Frisch, Henry J., Heintz, Mary, Oberla, Eric, Pastika, Nathaniel J., Rico-Aniles, Hector D., Rubinov, Paul M., Wang, Xiaoran, Yeung, Yui Man Richmond, Zimmerman, Tom N.
1 ps timing resolution is the entry point to signature based searches relying on secondary/tertiary vertices and particle identification. We describe a preliminary design for PSEC5, an 8-channel 40 GS/s waveform-sampling ASIC in the TSMC 65 nm proces
Externí odkaz:
http://arxiv.org/abs/2407.09575
Autor:
Gong, Cheng, Cooper, Erica, Wang, Xin, Qiang, Chunyu, Geng, Mengzhe, Wells, Dan, Wang, Longbiao, Dang, Jianwu, Tessier, Marc, Pine, Aidan, Richmond, Korin, Yamagishi, Junichi
Self-supervised learning (SSL) representations from massively multilingual models offer a promising solution for low-resource language speech tasks. Despite advancements, language adaptation in TTS systems remains an open problem. This paper explores
Externí odkaz:
http://arxiv.org/abs/2406.08911