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pro vyhledávání: '"Kim, TaeHyun"'
In this paper, we study the requirement for quantum random access memory (QRAM) in quantum lattice sieving, a fundamental algorithm for lattice-based cryptanalysis. First, we obtain a lower bound on the cost of quantum lattice sieving with a bounded
Externí odkaz:
http://arxiv.org/abs/2410.15565
Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the
Externí odkaz:
http://arxiv.org/abs/2406.06935
Mixture-of-Experts (MoE) large language models (LLM) have memory requirements that often exceed the GPU memory capacity, requiring costly parameter movement from secondary memories to the GPU for expert computation. In this work, we present Mixture o
Externí odkaz:
http://arxiv.org/abs/2405.18832
Autor:
Kim, Taehyun, Gadotti, Dimitri A., Querejeta, Miguel, Pérez, Isabel, Zurita, Almudena, Neumann, Justus, van de Ven, Glenn, Méndez-Abreu, Jairo, de Lorenzo-Cáceres, Adriana, Sánchez-Blázquez, Patricia, Fragkoudi, Francesca, Martins, Lucimara P., Silva-Lima, Luiz A., Kim, Woong-Tae, Park, Myeong-gu
Bars drive gas inflow. As the gas flows inwards, shocks and shear occur along the bar dust lanes. Such shocks and shear can affect the star formation and change the gas properties. For four barred galaxies, we present H{\alpha} velocity gradient maps
Externí odkaz:
http://arxiv.org/abs/2405.00107
Autor:
Kim, Taehyun, Wei, Xuehui, Chariton, Stella, Prakapenka, Vitali B., Ryu, Young-Jay, Yang, Shize, Shim, Sang-Heon
Publikováno v:
Proceedings of the National Academy of Sciences 120, no. 52 (2023): e2309786120
Many sub-Neptune exoplanets have been believed to be composed of a thick hydrogen-dominated atmosphere and a high-temperature heavier-element-dominant core. From an assumption that there is no chemical reaction between hydrogen and silicates/metals a
Externí odkaz:
http://arxiv.org/abs/2401.02637
Autor:
Menéndez-Delmestre, Karín, Gonçalves, Thiago S., Sheth, Kartik, de Lima, Tomás Düringer Jacques, Kim, Taehyun, Gadotti, Dimitri A., Schinnerer, Eva, Athanassoula, E., Bosma, Albert, Elmegreen, Debra Meloy, Knapen, Johan H., Machado, Rubens E. G., Salo, Heikki
The redshift evolution of bars is an important signpost of the dynamic maturity of disk galaxies. To characterize the intrinsic evolution safe from band-shifting effects, it is necessary to gauge how bar properties vary locally as a function of wavel
Externí odkaz:
http://arxiv.org/abs/2312.04545
Autor:
Lee, Woojun, Chung, Daun, Jeon, Honggi, Cho, Beomgeun, Choi, KwangYeul, Yoo, SeungWoo, Jung, Changhyun, Jeong, Junho, Kim, Changsoon, Cho, Dong-Il "Dan'', Kim, Taehyun
Publikováno v:
Phys. Rev. A 109, 043106 (2024)
Ion trap systems built upon microfabricated chips have emerged as a promising platform for quantum computing to achieve reproducible and scalable structures. However, photo-induced charging of materials in such chips can generate undesired stray elec
Externí odkaz:
http://arxiv.org/abs/2312.00059
Autor:
Conte, Zoe A. Le, Gadotti, Dimitri A., Ferreira, Leonardo, Conselice, Christopher J., de Sá-Freitas, Camila, Kim, Taehyun, Neumann, Justus, Fragkoudi, Francesca, Athanassoula, E., Adams, Nathan J.
The presence of a stellar bar in a disc galaxy indicates that the galaxy hosts in its main part a dynamically settled disc and that bar-driven processes are taking place in shaping its evolution. Studying the cosmic evolution of the bar fraction in d
Externí odkaz:
http://arxiv.org/abs/2309.10038
Autor:
Lee, Woojun, Chung, Daun, Kang, Jiyong, Jeon, Honggi, Jung, Changhyun, Cho, Dong-Il "Dan", Kim, Taehyun
Publikováno v:
Opt. Express 31 (2023) 33787-33798
Excess micromotion is detrimental to accurate qubit control of trapped ions, thus measuring and minimizing it is crucial. In this paper, we present a simple approach for measuring and suppressing excess micromotion of trapped ions by leveraging the e
Externí odkaz:
http://arxiv.org/abs/2306.05837
Autor:
Xu, Mingle, Kim, Hyongsuk, Yang, Jucheng, Fuentes, Alvaro, Meng, Yao, Yoon, Sook, Kim, Taehyun, Park, Dong Sun
Recent advancements in deep learning have brought significant improvements to plant disease recognition. However, achieving satisfactory performance often requires high-quality training datasets, which are challenging and expensive to collect. Conseq
Externí odkaz:
http://arxiv.org/abs/2305.11533