Zobrazeno 1 - 10
of 1 587
pro vyhledávání: '"Priore P"'
Autor:
Gilson, Aidan, Ai, Xuguang, Xie, Qianqian, Srinivasan, Sahana, Pushpanathan, Krithi, Singer, Maxwell B., Huang, Jimin, Kim, Hyunjae, Long, Erping, Wan, Peixing, Del Priore, Luciano V., Ohno-Machado, Lucila, Xu, Hua, Liu, Dianbo, Adelman, Ron A., Tham, Yih-Chung, Chen, Qingyu
Large Language Models (LLMs) are poised to revolutionize healthcare. Ophthalmology-specific LLMs remain scarce and underexplored. We introduced an open-source, specialized LLM for ophthalmology, termed Language Enhanced Model for Eye (LEME). LEME was
Externí odkaz:
http://arxiv.org/abs/2410.03740
Autor:
Liu, Chen, Xu, Ke, Shen, Liangbo L., Huguet, Guillaume, Wang, Zilong, Tong, Alexander, Bzdok, Danilo, Stewart, Jay, Wang, Jay C., Del Priore, Lucian V., Krishnaswamy, Smita
Advances in medical imaging technologies have enabled the collection of longitudinal images, which involve repeated scanning of the same patients over time, to monitor disease progression. However, predictive modeling of such data remains challenging
Externí odkaz:
http://arxiv.org/abs/2406.14794
Autor:
Roberta Priore
Publikováno v:
TECA, Vol 14, Iss 9ns, Pp 327-339 (2024)
The paper examines the Leopardian materials acquired and preserved by Loris Bononi at the library of the Centro Studi Umanistici Niccolò V in Castiglione del Terziere. The discourse opens by investigating the relationship between Bononi and Leopardi
Externí odkaz:
https://doaj.org/article/37b5389ea60244f1b5f21ec4fe36bb0d
Autor:
Priore, Shawn, Oishi, Meeko
While techniques have been developed for chance constrained stochastic optimal control using sample disturbance data that provide a probabilistic confidence bound for chance constraint satisfaction, far less is known about how to use sample data in a
Externí odkaz:
http://arxiv.org/abs/2303.16981
Autor:
Priore, Shawn, Oishi, Meeko
We propose an open loop methodology based on sample statistics to solve chance constrained stochastic optimal control problems with probabilistic safety guarantees for linear systems where the additive Gaussian noise has unknown mean and covariance.
Externí odkaz:
http://arxiv.org/abs/2303.13036
Autor:
Priore, Shawn, Oishi, Meeko
While many techniques have been developed for chance constrained stochastic optimal control with Gaussian disturbance processes, far less is known about computationally efficient methods to handle non-Gaussian processes. In this paper, we develop a m
Externí odkaz:
http://arxiv.org/abs/2303.12295
Autor:
Priore, Shawn, Oishi, Meeko
This work proposes an open-loop methodology to solve chance constrained stochastic optimal control problems for linear systems with a stochastic control matrix. We consider a joint chance constraint for polytopic time-varying target sets under moment
Externí odkaz:
http://arxiv.org/abs/2302.01863
Autor:
Helder S. Lopes, Marina C. Waiteman, Liliam B. Priore, Neal R. Glaviano, David M. Bazett-Jones, Ronaldo V. Briani, Fábio M. Azevedo
Publikováno v:
Journal of Sport and Health Science, Vol 13, Iss 4, Pp 521-536 (2024)
Background: Impairments in hamstring strength, flexibility, and morphology have been associated with altered knee biomechanics, pain, and function. Determining the presence of these impairments in individuals with gradual-onset knee disorders is impo
Externí odkaz:
https://doaj.org/article/d2701f27d4bc412c8b450f1d44274c38
Autor:
Roberta Priore
Publikováno v:
Prassi Ecdotiche della Modernità Letteraria, Iss 9 (2024)
Nell’affrontare la storia del rapporto di Cesare Pavese con la lingua e la cultura greca, il contributo si concentra sulle traduzioni private svolte durante il confino a Brancaleone calabro (1835-1836). Questo periodo segna un punto di svolta nell
Externí odkaz:
https://doaj.org/article/3c8e3b6081ee4d23b6880f619e588f8e
We propose an open loop control scheme for linear time invariant systems perturbed by multivariate $t$ disturbances through the use of quantile reformulations. The multivariate $t$ disturbance is motivated by heavy tailed phenomena that arise in mult
Externí odkaz:
http://arxiv.org/abs/2210.09479