Zobrazeno 1 - 10
of 417
pro vyhledávání: '"Ventola P"'
Autor:
Wang, Jiyao, Dvornek, Nicha C., Duan, Peiyu, Staib, Lawrence H., Ventola, Pamela, Duncan, James S.
Task-based fMRI uses actions or stimuli to trigger task-specific brain responses and measures them using BOLD contrast. Despite the significant task-induced spatiotemporal brain activation fluctuations, most studies on task-based fMRI ignore the task
Externí odkaz:
http://arxiv.org/abs/2406.12065
Publikováno v:
Neuropsychiatric Disease and Treatment, Vol Volume 13, Pp 1613-1626 (2017)
Jiedi Lei, Pamela Ventola Yale Child Study Center, Yale University School of Medicine, New Haven, CT, USA Abstract: Pivotal response treatment (PRT) is an evidence-based behavioral intervention based on applied behavior analysis principles aimed to i
Externí odkaz:
https://doaj.org/article/9615635e35fd41c7ad90330f613aabf5
Autor:
Ivana Ventola, Marianna Balasco, Michele De Girolamo, Luigi Falco, Marilena Filippucci, Laura Hillmann, Gerardo Romano, Vincenzo Serlenga, Tony Alfredo Stabile, Angelo Strollo, Andrea Tallarico, Simona Tripaldi, Thomas Zieke, Agata Siniscalchi
Publikováno v:
Geoscience Data Journal, Vol 11, Iss 4, Pp 863-872 (2024)
Abstract The seismic‐electromagnetic phenomenon entails the generation of transient electromagnetic signals, which can be observed both simultaneously (co‐seismic) and preceding (pre‐seismic) a seismic wave arrival. Following the most accredite
Externí odkaz:
https://doaj.org/article/654ce4ef01964ac3964a2c19bace7313
Probabilistic circuits (PCs) are models that allow exact and tractable probabilistic inference. In contrast to neural networks, they are often assumed to be well-calibrated and robust to out-of-distribution (OOD) data. In this paper, we show that PCs
Externí odkaz:
http://arxiv.org/abs/2302.06544
We present DAFNe, a Dense one-stage Anchor-Free deep Network for oriented object detection. As a one-stage model, it performs bounding box predictions on a dense grid over the input image, being architecturally simpler in design, as well as easier to
Externí odkaz:
http://arxiv.org/abs/2109.06148
Autor:
Li, Beibin, Nuechterlein, Nicholas, Barney, Erin, Foster, Claire, Kim, Minah, Mahony, Monique, Atyabi, Adham, Feng, Li, Wang, Quan, Ventola, Pamela, Shapiro, Linda, Shic, Frederick
Identifying oculomotor behaviors relevant for eye-tracking applications is a critical but often challenging task. Aiming to automatically learn and extract knowledge from existing eye-tracking data, we develop a novel method that creates rich represe
Externí odkaz:
http://arxiv.org/abs/2108.05025
Autor:
Federica Ventola
Publikováno v:
Noctua, Vol 11, Iss 1, Pp 49-74 (2024)
The 14th-century Dominican theologian and philosopher Durand of Saint-Pourçain was among the intellectuals who took part in the medieval debate on virginity, especially on the relationship between virginity and marriage. This paper discusses a quest
Externí odkaz:
https://doaj.org/article/afb545b82e164c3a95aca2f668264912
Molecular characterization of Yersinia enterocolitica strains to evaluate virulence associated genes
Autor:
Elisabetta Delibato, Eleonora Ventola, Sarah Lovari, Silvana Farneti, Guido Finazzi, Slawomir Owczarek, Bilei Stefano
Publikováno v:
Annali dell'Istituto Superiore di Sanità, Vol 59, Iss 4, Pp 280-285 (2023)
Introduction. Yersinia enterocolitica (Ye) species is divided into 6 biotypes (BT), 1A, 1B, 2, 3, 4, 5 classified based on biochemical reactions and about 70 serotypes, classified based on the structure of the lipopolysaccharide O-antigen. The BT1A i
Externí odkaz:
https://doaj.org/article/64087b681f594891b3c15ae97e20e3ca
Time series forecasting is a relevant task that is performed in several real-world scenarios such as product sales analysis and prediction of energy demand. Given their accuracy performance, currently, Recurrent Neural Networks (RNNs) are the models
Externí odkaz:
http://arxiv.org/abs/2106.04148
Autor:
Wainakh, Aidmar, Ventola, Fabrizio, Müßig, Till, Keim, Jens, Cordero, Carlos Garcia, Zimmer, Ephraim, Grube, Tim, Kersting, Kristian, Mühlhäuser, Max
Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the
Externí odkaz:
http://arxiv.org/abs/2105.09369