Zobrazeno 1 - 10
of 4 564
pro vyhledávání: '"Carlos, Garcia"'
Autor:
Katyara, Sunny, Sharma, Suchita, Damacharla, Praveen, Santiago, Carlos Garcia, Dhirani, Lubina, Chowdhry, Bhawani Shankar
As the manufacturing industry shifts from mass production to mass customization, there is a growing emphasis on adopting agile, resilient, and human-centric methodologies in line with the directives of Industry 5.0. Central to this transformation is
Externí odkaz:
http://arxiv.org/abs/2409.10784
Autor:
Katyara, Sunny, Sharma, Suchita, Damacharla, Praveen, Santiago, Carlos Garcia, O'Farrell, Francis, Long, Philip
Designing an efficient and resilient human-robot collaboration strategy that not only upholds the safety and ergonomics of shared workspace but also enhances the performance and agility of collaborative setup presents significant challenges concernin
Externí odkaz:
http://arxiv.org/abs/2409.08166
Data following an interval structure are increasingly prevalent in many scientific applications. In medicine, clinical events are often monitored between two clinical visits, making the exact time of the event unknown and generating outcomes with a r
Externí odkaz:
http://arxiv.org/abs/2408.16381
Autor:
Katsarou, Styliani, Carminati, Francesca, Dlask, Martin, Braojos, Marta, Patra, Lavena, Perkins, Richard, Ling, Carlos Garcia, Paskevich, Maria
A good understanding of player preferences is crucial for increasing content relevancy, especially in mobile games. This paper illustrates the use of attentive models for producing item recommendations in a mobile game scenario. The methodology compr
Externí odkaz:
http://arxiv.org/abs/2408.06799
In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB) as input to be classified into one of five defined classes, normal im
Externí odkaz:
http://arxiv.org/abs/2406.08231
We introduce a novel estimator for predicting outcomes in the presence of hidden confounding across different distributional settings without relying on regularization or a known causal structure. Our approach is based on parametrizing the dependence
Externí odkaz:
http://arxiv.org/abs/2402.15502
Autor:
Wang, Q. Daniel, Diaz, Carlos Garcia, Kamieneski, Patrick S., Harrington, Kevin C., Yun, Min S., Foo, Nicholas, Frye, Brenda L., Jimenez-Andrade, Eric F., Liu, Daizhong, Lowenthal, James D., Pampliega, Belen Alcalde, Pascale, Massimo, Vishwas, Amit, Gurwell, Mark A.
Hyper-luminous infrared galaxies (HyLIRGs) are the most extreme star-forming systems observed in the early Universe, and their properties still elude comprehensive understanding. We have undertaken a large XMM-Newton observing program to probe the to
Externí odkaz:
http://arxiv.org/abs/2312.05442
Autor:
José Carlos Garcia, Jr, MD, PhD, Cindy Yukie Nakano Schincariol, MD, Ricardo Berriel Mendes, MD, Paulo Cavalcante Muzy, MD
Publikováno v:
JSES International, Vol 8, Iss 6, Pp 1169-1174 (2024)
Background: Surgical procedures to treat anterior shoulder instability are essentially divided into those for significant bone loss and those without relevant bone loss. However, there is a gray area between these procedures that would not require bo
Externí odkaz:
https://doaj.org/article/c24ab1759b53459c89eaec639a29092c
