Zobrazeno 1 - 10
of 44 036
pro vyhledávání: '"YAMANE, A."'
Autor:
Yamane, Kazuki, Matsushita, Yoshitaka, Adachi, Shintaro, Matsumoto, Ryo, Terashima, Kensei, Hiroto, Takanobu, Sakurai, Hiroya, Takano, Yoshihiko
A novel oxychloride, Sr$_{3}$Ni$_{2}$O$_{5}$Cl$_{2}$, was synthesized for the first time under high pressure of 10 GPa at 1400 ${}^\circ$C, motivated by a theoretical prediction of its potential superconductivity under ambient pressure. Small single
Externí odkaz:
http://arxiv.org/abs/2412.09093
Because imitation learning relies on human demonstrations in hard-to-simulate settings, the inclusion of force control in this method has resulted in a shortage of training data, even with a simple change in speed. Although the field of data augmenta
Externí odkaz:
http://arxiv.org/abs/2412.03252
Autor:
Matsumoto, Ryo, Nakano, Akitoshi, Yamamoto, Takafumi D, Terashima, Kensei, Yamane, Kazuki, Ohkuma, Masahiro, Terasaki, Ichiro, Takano, Yoshihiko
The emergence of a second dome in the superconducting phase through pressure-driven manipulation of crystal structures in materials has attracted considerable attention. Transition metal chalcogenides (TMCs) represent a highly promising platform, as
Externí odkaz:
http://arxiv.org/abs/2411.01149
Autor:
Cooper, Avi, Kato, Keizo, Shih, Chia-Hsien, Yamane, Hiroaki, Vinken, Kasper, Takemoto, Kentaro, Sunagawa, Taro, Yeh, Hao-Wei, Yamanaka, Jin, Mason, Ian, Boix, Xavier
Visual Language Models (VLMs) are now increasingly being merged with Large Language Models (LLMs) to enable new capabilities, particularly in terms of improved interactivity and open-ended responsiveness. While these are remarkable capabilities, the
Externí odkaz:
http://arxiv.org/abs/2410.14690
Optical flow estimation is a fundamental and long-standing visual task. In this work, we present a novel method, dubbed HMAFlow, to improve optical flow estimation in challenging scenes, particularly those involving small objects. The proposed model
Externí odkaz:
http://arxiv.org/abs/2409.05531
Autor:
Matsumoto, Ryo, Yamane, Kazuki, Tadano, Terumasa, Terashima, Kensei, Shinmei, Toru, Irifune, Tetsuo, Takano, Yoshihiko
The exploration of superconductors in metastable phases by manipulating crystal structures through high-pressure techniques has attracted significant interest in materials science to achieve a high critical temperature ($T_c$). In this study, we repo
Externí odkaz:
http://arxiv.org/abs/2409.03409
Autor:
Ramakrishnan, Sitaram, Yamakawa, Tatsuya, Oishi, Ryohei, Yamane, Soichiro, Ikeda, Atsutoshi, Kado, Masaki, Shimura, Yasuyuki, Takabatake, Toshiro, Onimaru, Takahiro, Shibata, Yasuhiro, Thamizhavel, Arumugam, Ramakrishnan, Srinivasan, Yonezawa, Shingo, Nohara, Minoru
Publikováno v:
J. Phys. Soc. Jpn. 93, 124709 (2024)
We report the crystal structures and superconductivity (SC) of LaPt$_{x}$Si$_{2-x}$ ($0.5 \leq x \leq 1.0$) that are solid solutions of LaSi$_{2}$ and LaPtSi with centrosymmetric tetragonal ($I4_{1}/amd$, $D_{4h}^{19}$, \#141) and non-centrosymmetric
Externí odkaz:
http://arxiv.org/abs/2408.17033
Autor:
Ueki, Yuta, Sakurai, Hiroya, Nagata, Hibiki, Yamane, Kazuki, Matsumoto, Ryo, Terashima, Kensei, Hirose, Keisuke, Ohta, Hiroto, Kato, Masaki, Takano, Yoshihiko
We successfully synthesized samples of La$_{3}$Ni$_{2}$O$_{7+\delta}$ ($\delta = -0.50$, $-0.16$, $0.00$, $+0.01$, and $+0.12$) and measured the resistance under extremely high pressures using a diamond anvil cell to establish the electronic phase di
Externí odkaz:
http://arxiv.org/abs/2408.04970
Autor:
Fukaya, Naoki, Yamane, Koki, Masuda, Shimpei, Ummadisingu, Avinash, Maeda, Shin-ichi, Takahashi, Kuniyuki
Robots operating in the real world face significant but unavoidable issues in object localization that must be dealt with. A typical approach to address this is the addition of compliance mechanisms to hardware to absorb and compensate for some of th
Externí odkaz:
http://arxiv.org/abs/2407.21245
Autor:
Inada, Yuki, Fujioka, Masaya, Morito, Haruhiko, Sugahara, Tohru, Yamane, Hisanori, Katsura, Yukari
When searching for novel inorganic materials, limiting the combination of constituent elements can greatly improve the search efficiency. In this study, we used machine learning to predict elemental combinations with high reactivity for materials dis
Externí odkaz:
http://arxiv.org/abs/2407.20549