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pro vyhledávání: '"Castro, Francisco"'
Ride-hailing platforms typically classify drivers as either employees or independent contractors. These classifications tend to emphasize either wage certainty or flexibility, but rarely both. We study an alternative or complementary approach: the \t
Externí odkaz:
http://arxiv.org/abs/2408.13421
Autor:
Morales-Navarro, Luis, Kafai, Yasmin B., Nguyen, Ha, DesPortes, Kayla, Vacca, Ralph, Matuk, Camillia, Silander, Megan, Amato, Anna, Woods, Peter, Castro, Francisco, Shaw, Mia, Akgun, Selin, Greenhow, Christine, Garcia, Antero
TikTok, a popular short video sharing application, emerged as the dominant social media platform for young people, with a pronounced influence on how young women and people of color interact online. The application has become a global space for youth
Externí odkaz:
http://arxiv.org/abs/2405.15437
Autor:
Dueñas-Pamplona, Jorge, Rodríguez-Aparicio, Sergio, Gonzalo, Alejandro, Bifulco, Savannah F., Castro, Francisco, Ferrera, Conrado, Flores, Óscar, Boyle, Patrick M., Sierra-Pallares, José, García, Javier García, del Álamo, Juan C.
Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It inc
Externí odkaz:
http://arxiv.org/abs/2310.05443
When working with generative artificial intelligence (AI), users may see productivity gains, but the AI-generated content may not match their preferences exactly. To study this effect, we introduce a Bayesian framework in which heterogeneous users ch
Externí odkaz:
http://arxiv.org/abs/2309.10448
We study electric vehicle (EV) fleet and charging infrastructure planning in a spatial setting. With customer requests arriving continuously at rate $\lambda$ throughout the day, we determine the minimum number of vehicles and chargers for a target s
Externí odkaz:
http://arxiv.org/abs/2306.10178
Autor:
Morales-Navarro, Luis, Kafai, Yasmin B., Castro, Francisco, Payne, William, DesPortes, Kayla, DiPaola, Daniella, Williams, Randi, Ali, Safinah, Breazeal, Cynthia, Lee, Clifford, Soep, Elisabeth, Long, Duri, Magerko, Brian, Solyst, Jaemarie, Ogan, Amy, Tatar, Cansu, Jiang, Shiyan, Chao, Jie, Rosé, Carolyn P., Vakil, Sepehr
Publikováno v:
Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023
Understanding how youth make sense of machine learning and how learning about machine learning can be supported in and out of school is more relevant than ever before as young people interact with machine learning powered applications everyday; while
Externí odkaz:
http://arxiv.org/abs/2305.02840
Autor:
Kwon, Heeseung, Castro, Francisco M., Marin-Jimenez, Manuel J., Guil, Nicolas, Alahari, Karteek
Vision Transformers (ViTs) have become a dominant paradigm for visual representation learning with self-attention operators. Although these operators provide flexibility to the model with their adjustable attention kernels, they suffer from inherent
Externí odkaz:
http://arxiv.org/abs/2211.16289