Zobrazeno 1 - 10
of 4 784
pro vyhledávání: '"WANG, XIAOJIE"'
Autor:
Suzuki, Hiromasa, Tsuji, Naomi, Kanemaru, Yoshiaki, Shidatsu, Megumi, Olivera-Nieto, Laura, Safi-Harb, Samar, Kimura, Shigeo S., de la Fuente, Eduardo, Casanova, Sabrina, Mori, Kaya, Wang, Xiaojie, Kato, Sei, Tateishi, Dai, Uchiyama, Hideki, Tanaka, Takaaki, Uchida, Hiroyuki, Inoue, Shun, Huang, Dezhi, Lemoine-Goumard, Marianne, Miura, Daiki, Ogawa, Shoji, Kobayashi, Shogo B., Done, Chris, Parra, Maxime, Trigo, María Díaz, Muñoz-Darias, Teo, Padilla, Montserrat Armas, Tomaru, Ryota, Ueda, Yoshihiro
A recent report on the detection of very-high-energy gamma rays from V4641 Sagittarii (V4641 Sgr) up to ~0.8 peta-electronvolt has made it the second confirmed "PeVatron" microquasar. Here we report on the observation of V4641 Sgr with X-Ray Imaging
Externí odkaz:
http://arxiv.org/abs/2412.08089
Autor:
Qi, Ruisheng, Wang, Xiaojie
In the present work, strong approximation errors are analyzed for both the spatial semi-discretization and the spatio-temporal fully discretization of stochastic wave equations (SWEs) with cubic polynomial nonlinearities and additive noises. The full
Externí odkaz:
http://arxiv.org/abs/2411.04359
Autor:
Wang, Zhizhao, Yang, Shuju, Wu, Kaihang, Wang, Xiaojie, Duan, Huizong, Liang, Yurong, Zhang, Xuefeng, Yeh, Hsien-Chi
Tilt-to-length (TTL) coupling is expected to be one of the major noise sources in the interferometric phase readouts in TianQin mission. Arising from the angular motion of spacecraft (SC) and the onboard movable optical subassemblies (MOSAs), TTL noi
Externí odkaz:
http://arxiv.org/abs/2410.20121
Large language models (LLMs) exhibit remarkable capabilities in natural language processing but face catastrophic forgetting when learning new tasks, where adaptation to a new domain leads to a substantial decline in performance on previous tasks. In
Externí odkaz:
http://arxiv.org/abs/2410.16801
Autor:
Dai, Lei, Wang, Xiaojie
In the present work, we delve into further study of numerical approximations of SDEs with non-globally monotone coefficients. We design and analyze a new family of stopped increment-tamed time discretization schemes of Euler, Milstein and order 1.5 t
Externí odkaz:
http://arxiv.org/abs/2410.04697
The customization of text-to-image models has seen significant advancements, yet generating multiple personalized concepts remains a challenging task. Current methods struggle with attribute leakage and layout confusion when handling multiple concept
Externí odkaz:
http://arxiv.org/abs/2408.03632
Physics-Informed Neural Networks (PINNs) are a machine learning technique for solving partial differential equations (PDEs) by incorporating PDEs as loss terms in neural networks and minimizing the loss function during training. Tomographic imaging,
Externí odkaz:
http://arxiv.org/abs/2407.17721
The present work introduces and investigates an explicit time discretization scheme, called the projected Euler method,to numerically approximate random periodic solutions of semi-linear SDEs under non-globally Lipschitz conditions. The existence of
Externí odkaz:
http://arxiv.org/abs/2406.16089
This paper is the second in a series of works on weak convergence of one-step schemes for solving stochastic differential equations (SDEs) with one-sided Lipschitz conditions. It is known that the super-linear coefficients may lead to a blowup of mom
Externí odkaz:
http://arxiv.org/abs/2406.14065
Autor:
Wu, Xiaoming, Wang, Xiaojie
This paper is concerned with long-time strong approximations of SDEs with non-globally Lipschitz coefficients.Under certain non-globally Lipschitz conditions, a long-time version of fundamental strong convergence theorem is established for general on
Externí odkaz:
http://arxiv.org/abs/2406.10582