Zobrazeno 1 - 10
of 78 282
pro vyhledávání: '"Koike A"'
Autor:
Koike, Takayuki
We investigate the formal principle for holomorphic line bundles on neighborhoods of an analytic subset of a complex manifold mainly when it can be realized as an open subset of a compact K\"ahler manifold.
Comment: 27 pages
Comment: 27 pages
Externí odkaz:
http://arxiv.org/abs/2409.20367
Autor:
Koike, Kenji
We determine projective equations of smooth complex cubic fourfolds with symplectic automorphisms by classifying 6-dimensional projective representations of Laza and Zheng's 34 groups. In particular, we determine the number of irreducible components
Externí odkaz:
http://arxiv.org/abs/2409.08448
Autor:
Li, Zhuohang, Lowy, Andrew, Liu, Jing, Koike-Akino, Toshiaki, Malin, Bradley, Parsons, Kieran, Wang, Ye
We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private information beyond training data reconstruction. Motivated by the low re
Externí odkaz:
http://arxiv.org/abs/2409.07291
In this paper, we extend the uniform $L^2$-estimate of $\bar{\partial}$-equations for flat nontrivial line bundles, proved for compact K\"ahler manifolds in the previous work, to compact complex manifolds. In the proof, by tracing the Dolbeault isomo
Externí odkaz:
http://arxiv.org/abs/2409.05300
D4: Text-guided diffusion model-based domain adaptive data augmentation for vineyard shoot detection
Autor:
Hirahara, Kentaro, Nakane, Chikahito, Ebisawa, Hajime, Kuroda, Tsuyoshi, Iwaki, Yohei, Utsumi, Tomoyoshi, Nomura, Yuichiro, Koike, Makoto, Mineno, Hiroshi
In an agricultural field, plant phenotyping using object detection models is gaining attention. However, collecting the training data necessary to create generic and high-precision models is extremely challenging due to the difficulty of annotation a
Externí odkaz:
http://arxiv.org/abs/2409.04060
Zero-point fluctuations in the background of a cosmic string provide an opportunity to study the effects of topology in quantum field theory. We use a scattering theory approach to compute quantum corrections to the energy density of a cosmic string,
Externí odkaz:
http://arxiv.org/abs/2409.03723
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of p
Externí odkaz:
http://arxiv.org/abs/2408.17354
Autor:
Li, Zhuohang, Lowy, Andrew, Liu, Jing, Koike-Akino, Toshiaki, Parsons, Kieran, Malin, Bradley, Wang, Ye
In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privacy risks of sharing gradients, our paper aims to provide a systematic a
Externí odkaz:
http://arxiv.org/abs/2408.16913
Autor:
Li, Yuanhao, Chen, Badong, Hu, Zhongxu, Suzuki, Keita, Bai, Wenjun, Koike, Yasuharu, Yamashita, Okito
Bayesian learning provides a unified skeleton to solve the electrophysiological source imaging task. From this perspective, existing source imaging algorithms utilize the Gaussian assumption for the observation noise to build the likelihood function
Externí odkaz:
http://arxiv.org/abs/2408.14843
Autor:
Mwange, Agnes N., Miyauchi, Yoshiki, Kambara, Taichi, Koike, Hiroaki, Hosogaya, Kazuyoshi, Maki, Atsuo
Leveraging empirical data is crucial in the development of accurate and reliable virtual models for the advancement of autonomous ship technologies and the optimization of port operations. This study presents an in-depth analysis of ship berthing and
Externí odkaz:
http://arxiv.org/abs/2408.13497