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Julius Wilhelm Zincgref (1591‑1635), der Heidelberger Jurist, gilt als ein Meister der kleinformatigen Zweckliteratur in seinem Jahrhundert. Aus seinem Œuvre ragt die besonders wirkungsreiche zweibändige Sammlung Der Teutschen scharpfsinnige klug
Autor:
Sharma, Saurabh, Dewangan, Lokesh, Panwar, Neelam, Kaur, Harmeen, Ojha, Devendra K., Yadav, Ramkesh, Verma, Aayushi, Baug, Tapas, Sinha, Tirthendu, Pandey, Rakesh, Ghosh, Arpan, Chand, Tarak
We have performed a detailed analysis on the Teutsch 76 (T76) open cluster using the deep near-infrared (NIR) observations taken with the TANSPEC instrument mounted on the 3.6m Devasthal Optical Telescope (DOT) along with the recently available high
Externí odkaz:
http://arxiv.org/abs/2302.04516
There is a growing interest in the automated characterization of open clusters using data from the Gaia mission. This work evidences the importance of choosing an appropriate sampling radius (the radius of the circular region around the cluster used
Externí odkaz:
http://arxiv.org/abs/2109.08175
Autor:
Skelton, Adam G., Meltzer, Martin I.
Publikováno v:
Journal of Public Health Management and Practice, 2017 Jul 01. 23(4), e14-e21.
Externí odkaz:
https://www.jstor.org/stable/48517298
Autor:
Joshi, Gireesh C.
The spatial morphological study of studied clusters is carried out through the identified probable members within them. The field stars decontamination is performed by the statistical cleaning approach (depends on the magnitude and colour of stars wi
Externí odkaz:
http://arxiv.org/abs/1705.02566
Publikováno v:
Astrophysics & Space Science. Jul2015, Vol. 358 Issue 1, p1-6. 6p.
Publikováno v:
In Advances in Space Research 1 January 2018 61(1):571-580
Bayesian Optimization (BO) is a data-driven strategy for minimizing/maximizing black-box functions based on probabilistic surrogate models. In the presence of safety constraints, the performance of BO crucially relies on tight probabilistic error bou
Externí odkaz:
http://arxiv.org/abs/2411.02253
Despite their success in various vision tasks, deep neural network architectures often underperform in out-of-distribution scenarios due to the difference between training and target domain style. To address this limitation, we introduce One-Shot Sty
Externí odkaz:
http://arxiv.org/abs/2410.00900
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