Zobrazeno 1 - 10
of 2 842
pro vyhledávání: '"Shibano A"'
Autor:
Han, Changhee, Shibano, Kyohei, Ozaki, Wataru, Osaki, Keishiro, Haraguchi, Takafumi, Hirahara, Daisuke, Kimura, Shumon, Kobayashi, Yasuyuki, Mogi, Gento
Deep Learning is advancing medical imaging Research and Development (R&D), leading to the frequent clinical use of Artificial Intelligence/Machine Learning (AI/ML)-based medical devices. However, to advance AI R&D, two challenges arise: 1) significan
Externí odkaz:
http://arxiv.org/abs/2403.06145
Autor:
Suegami, Sora, Shibano, Kyohei
Publikováno v:
2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), Dubai, United Arab Emirates, 2023, pp. 1-2.
We proposed a new construction for contract wallets, smart contract applications that allow users to control their crypto assets. Users can manipulate their crypto assets by simply sending emails with no need to manage keys. These emails are verified
Externí odkaz:
http://arxiv.org/abs/2312.04173
Publikováno v:
Journal of Magnesium and Alloys, Vol 12, Iss 8, Pp 3173-3179 (2024)
In this study, the mechanical behavior of crystal group of hexagonal close-packed (hcp; α phase) and body-centered cubic (bcc; β phase) during tensile loading was investigated to elucidate the mechanism from elastic to plastic deformation transitio
Externí odkaz:
https://doaj.org/article/f502b8cf66824e56bad8bfdab2a41a9d
Autor:
Shoji Kuriyama, Kazuhiro Imai, Hiroshi Nanjo, Shinogu Takashima, Hidenobu Iwai, Ryo Demura, Haruka Suzuki, Yuzu Harata, Sumire Shibano, Yoshihiro Minamiya
Publikováno v:
Thoracic Cancer, Vol 15, Iss 21, Pp 1681-1684 (2024)
Abstract When a mass occurs at the staple line following lung resection, it can be difficult to distinguish between local cancer recurrence and granuloma. We present a case of a staple‐line granuloma with 18F‐fluorodeoxyglucose‐positron emissio
Externí odkaz:
https://doaj.org/article/9058586f1ba141d09b7da74b653975fc
The system proposed in this study uses zero-knowledge proof (ZKP) to verify the traceability of wood recorded in a public blockchain. Wood is a byproduct of several states, ranging from standing trees to logs, lumber, and wood products (hereinafter `
Externí odkaz:
http://arxiv.org/abs/2211.11136
Autor:
Kazuhiro Imai, Nobuyasu Kurihara, Motoko Konno, Naoko Mori, Shinogu Takashima, Shoji Kuriyama, Ryo Demura, Haruka Suzuki, Yuzu Harata, Tatsuki Fujibayashi, Sumire Shibano, Akiyuki Wakita, Yushi Nagaki, Yusuke Sato, Kyoko Nomura, Yoshihiro Minamiya
Publikováno v:
Cancer Imaging, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background Accurate clinical staging is crucial for selection of optimal oncological treatment strategies in non-small cell lung cancer (NSCLC). Although brain MRI, bone scintigraphy and whole-body PET/CT play important roles in detecting di
Externí odkaz:
https://doaj.org/article/78aa33673e4449d68ef2e022c888edaa
Publikováno v:
Case Studies in Construction Materials, Vol 20, Iss , Pp e02984- (2024)
In order to maintain serviceability and reliability of concrete structures, it is essential to assess their condition as concrete structures deteriorate in time. Cracks develop in concrete due to several reasons such as severe loading, environmental
Externí odkaz:
https://doaj.org/article/70f196e7f9444071b046a9d390062ce6
Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been
Externí odkaz:
http://arxiv.org/abs/2203.12587
ASR systems designed for native English (L1) usually underperform on non-native English (L2). To address this performance gap, \textbf{(i)} we extend our previous work to investigate fine-tuning of a pre-trained wav2vec 2.0 model \cite{baevski2020wav
Externí odkaz:
http://arxiv.org/abs/2202.05209
Autor:
Shibano, Toshiko, Zhang, Xinyi, Li, Mia Taige, Cho, Haejin, Sullivan, Peter, Abdul-Mageed, Muhammad
To address the performance gap of English ASR models on L2 English speakers, we evaluate fine-tuning of pretrained wav2vec 2.0 models (Baevski et al., 2020; Xu et al., 2021) on L2-ARCTIC, a non-native English speech corpus (Zhao et al., 2018) under d
Externí odkaz:
http://arxiv.org/abs/2110.00678