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pro vyhledávání: '"Park, Yubin"'
Autor:
Zhong, Janet, Wang, Kai, Park, Yubin, Asadchy, Viktar, Wojcik, Charles C., Dutt, Avik, Fan, Shanhui
Publikováno v:
Phys. Rev. B 104, 125416 (2021)
We show that two-dimensional non-Hermitian photonic crystals made of lossy material can exhibit non-trivial point gap topology in terms of topological winding in its complex frequency band structure. Such crystals can be either made of lossy isotropi
Externí odkaz:
http://arxiv.org/abs/2107.08637
Publikováno v:
ACS Photonics 2021, 8, 2417-2424
Kirchhoff's law of thermal radiation imposes a constraint on photon-based energy harvesting processes since part of the incident energy flux is inevitably emitted back to the source. By breaking the reciprocity of the system, it is possible to overco
Externí odkaz:
http://arxiv.org/abs/2105.08954
Autor:
Hong, Jin-Hwan, Kim, Dongbhin, Park, Yubin, Ryu, Jinha, Lee, Saemi, Yoo, Jongmin, Choi, Byoungdeog
Publikováno v:
In Materials Science in Semiconductor Processing January 2024 169
Akademický článek
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Akademický článek
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Autor:
Park, Yubin, Ho, Joyce C.
Stochastic Gradient TreeBoost is often found in many winning solutions in public data science challenges. Unfortunately, the best performance requires extensive parameter tuning and can be prone to overfitting. We propose PaloBoost, a Stochastic Grad
Externí odkaz:
http://arxiv.org/abs/1807.08383
Autor:
Park, Yubin1 (AUTHOR), Fan, Shanhui1,2 (AUTHOR) shanhui@stanford.edu
Publikováno v:
Applied Physics Letters. 12/9/2024, Vol. 125 Issue 24, p1-5. 5p.
In many healthcare settings, intuitive decision rules for risk stratification can help effective hospital resource allocation. This paper introduces a novel variant of decision tree algorithms that produces a chain of decisions, not a general tree. O
Externí odkaz:
http://arxiv.org/abs/1606.05325
We propose a novel statistical model to answer three challenges in direct marketing: which channel to use, which offer to make, and when to offer. There are several potential applications for the proposed model, for example, developing personalized m
Externí odkaz:
http://arxiv.org/abs/1507.01135
Autor:
Park, Yubin, Ghosh, Joydeep
We propose a categorical data synthesizer with a quantifiable disclosure risk. Our algorithm, named Perturbed Gibbs Sampler, can handle high-dimensional categorical data that are often intractable to represent as contingency tables. The algorithm ext
Externí odkaz:
http://arxiv.org/abs/1312.5370