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pro vyhledávání: '"Taylor, Sarah A"'
Autor:
Taylor, Sarah
This paper examines a government's strategic resource allocation choices when facing an opposing group whose military power is uncertain. We investigate how this uncertainty affects the government's decision to divide resources in a way that either g
Externí odkaz:
http://arxiv.org/abs/2410.14362
This paper studies the general class of games where agents: (1) are embedded on a network, (2) have two possible actions, and (3) these actions are strategic complements. We use a measure of network cohesiveness -- the k-core -- to provide a novel ch
Externí odkaz:
http://arxiv.org/abs/2406.04540
Co-speech gesturing is an important modality in conversation, providing context and social cues. In character animation, appropriate and synchronised gestures add realism, and can make interactive agents more engaging. Historically, methods for autom
Externí odkaz:
http://arxiv.org/abs/2405.08042
Autor:
MacFawn, Ian P., Magnon, Grant, Gorecki, Grace, Kunning, Sheryl, Rashid, Rufiaat, Kaiza, Medard Ernest, Atiya, Huda, Ruffin, Ayana T., Taylor, Sarah, Soong, T. Rinda, Bao, Riyue, Coffman, Lan G., Bruno, Tullia C.
Publikováno v:
In Cancer Cell 11 November 2024 42(11):1864-1881
Publikováno v:
In The Journal of Pediatrics November 2024 274
Autor:
Queirós, Ana M., Talbot, Elizabeth, Msuya, Flower E., Kuguru, Baraka, Jiddawi, Narriman, Mahongo, Shigalla, Shaghude, Yohana, Muhando, Christopher, Chundu, Elias, Jacobs, Zoe, Sailley, Sevrine, Virtanen, Elina A., Viitasalo, Markku, Osuka, Kennedy, Aswani, Shankar, Coupland, Jack, Wilson, Rob, Taylor, Sarah, Fernandes-Salvador, Jose A., Van Gennip, Simon, Senkondo, Edward, Meddard, Modesta, Popova, Ekaterina
Publikováno v:
In Science of the Total Environment 15 October 2024 947
We propose SUB-Depth, a universal multi-task training framework for self-supervised monocular depth estimation (SDE). Depth models trained with SUB-Depth outperform the same models trained in a standard single-task SDE framework. By introducing an ad
Externí odkaz:
http://arxiv.org/abs/2111.09692
Self-supervised learning for depth estimation uses geometry in image sequences for supervision and shows promising results. Like many computer vision tasks, depth network performance is determined by the capability to learn accurate spatial and seman
Externí odkaz:
http://arxiv.org/abs/2110.09482
Autor:
Oscherwitz, Max E., Godinich, Brandon M., Patel, Raj H., Avila, Christina, Neman, Sophia, Saberi, Shahin A., Mariencheck, Maria Chiara, Jorizzo, Joesph L., Pichardo, Rita, Taylor, Sarah, França, Katlein, Trinidad, John, Feldman, Steven R.
Publikováno v:
In JAAD Reviews September 2024 1:9-21
Publikováno v:
In The Journal of Pediatrics September 2024 272