Zobrazeno 1 - 10
of 2 063
pro vyhledávání: '"Pan Ling"'
Publikováno v:
Journal of Aeronautical Materials, Vol 44, Iss 4, Pp 16-27 (2024)
The types,preparation methods,thermal and mechanical properties of rare earth hafnate materials and their corrosion behaviors exposed to low melting point silicate(CMAS) and high temperature water vapor are summarized. The previous investigat
Externí odkaz:
https://doaj.org/article/47087f519f51426289f291a12ee832d6
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In the Artificial Intelligence era, organizations face unprecedented challenges and opportunities. The use of technology has not only changed the way work is done, but also had a significant impact on employee behavior patterns and performance. The i
Externí odkaz:
https://doaj.org/article/d887e0fa003941759882ac1b1622d868
Publikováno v:
European Physical Journal C: Particles and Fields, Vol 81, Iss 9, Pp 1-15 (2021)
Abstract Motivated by the observation of the first hidden charm pentaquarks by the LHCb collaboration in 2015 and the updated analysis with an order-of-magnitude larger data set in 2019, we estimate their cross sections for the prompt production as w
Externí odkaz:
https://doaj.org/article/45fe0d31587e4c89af14316e0456bf29
Publikováno v:
Dianzi Jishu Yingyong, Vol 47, Iss 4, Pp 9-16 (2021)
In view of the lack of sufficient computing, storage and communication network resources for computing equipment at the edge of inclement industrial environments, and the requirement for ms-level data processing response, the agile intelligent system
Externí odkaz:
https://doaj.org/article/bf840d77a95e4f07a681f2e5fa59e19c
The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sample objects with probability proportional to an unnormalized reward function. GFlowNets share a strong resemblan
Externí odkaz:
http://arxiv.org/abs/2406.02213
Generative Flow Networks (GFlowNets) are amortized sampling methods for learning a stochastic policy to sequentially generate compositional objects with probabilities proportional to their rewards. GFlowNets exhibit a remarkable ability to generate d
Externí odkaz:
http://arxiv.org/abs/2406.01150
Generative Flow Networks (GFlowNets), a new family of probabilistic samplers, have recently emerged as a promising framework for learning stochastic policies that generate high-quality and diverse objects proportionally to their rewards. However, exi
Externí odkaz:
http://arxiv.org/abs/2406.01901
Combinatorial optimization problems are pivotal across many fields. Among these, Quadratic Unconstrained Binary Optimization (QUBO) problems, central to fields like portfolio optimization, network design, and computational biology, are NP-hard and re
Externí odkaz:
http://arxiv.org/abs/2406.03502
Autor:
Dan Yuan, Meijing Liu, Yiping Li, Peng Jia, Ling Su, Li Ye, Yan Zhang, Pan Ling, Chang Zhou, Shu Liang, Hong Yang, Honglu Liu, Shujuan Yang
Publikováno v:
BMC Infectious Diseases, Vol 20, Iss 1, Pp 1-11 (2020)
Abstract Background The drug resistance and the virologic failure of antiretroviral therapy (ART) are quite severe in Liangshan. A better understanding of the virologic failure of ART and the HIV-1 transmission network dynamics is essential for the s
Externí odkaz:
https://doaj.org/article/5c73afaf72444711b97b0e48e1f1d726
Learning a generalist embodied agent capable of completing multiple tasks poses challenges, primarily stemming from the scarcity of action-labeled robotic datasets. In contrast, a vast amount of human videos exist, capturing intricate tasks and inter
Externí odkaz:
http://arxiv.org/abs/2402.14407