Zobrazeno 1 - 10
of 81 527
pro vyhledávání: '"Gui, P."'
Autor:
Suehara, Taikan, Tagami, Risako, Gui, Lai, Murata, Tatsuki, Tanabe, Tomohiko, Ootani, Wataru, Ishino, Masaya
Deep learning can give a significant impact on physics performance of electron-positron Higgs factories such as ILC and FCCee. We are working on two topics on event reconstruction to apply deep learning. The first is jet flavor tagging, in which we a
Externí odkaz:
http://arxiv.org/abs/2410.08772
Autor:
Huang, De-Xing, Zhou, Xiao-Hu, Gui, Mei-Jiang, Xie, Xiao-Liang, Liu, Shi-Qi, Wang, Shuang-Yi, Li, Hao, Xiang, Tian-Yu, Hou, Zeng-Guang
Iodinated contrast agents are widely utilized in numerous interventional procedures, yet posing substantial health risks to patients. This paper presents CAS-GAN, a novel GAN framework that serves as a ``virtual contrast agent" to synthesize X-ray an
Externí odkaz:
http://arxiv.org/abs/2410.08490
Current large multimodal models (LMMs) face challenges in grounding, which requires the model to relate language components to visual entities. Contrary to the common practice that fine-tunes LMMs with additional grounding supervision, we find that t
Externí odkaz:
http://arxiv.org/abs/2410.08209
Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework, reliably provides valid predictive inference for black-box models. CP constructs prediction sets that contain the true output with a specified probability. Howe
Externí odkaz:
http://arxiv.org/abs/2410.06494
Identifying causal relations from purely observational data typically requires additional assumptions on relations and/or noise. Most current methods restrict their analysis to datasets that are assumed to have pure linear or nonlinear relations, whi
Externí odkaz:
http://arxiv.org/abs/2410.05890
In the past, Retrieval-Augmented Generation (RAG) methods split text into chunks to enable language models to handle long documents. Recent tree-based RAG methods are able to retrieve detailed information while preserving global context. However, wit
Externí odkaz:
http://arxiv.org/abs/2410.04790
Despite the strong performance in many computer vision tasks, Convolutional Neural Networks (CNNs) can sometimes struggle to efficiently capture long-range, complex non-linear dependencies in deeper layers of the network. We address this limitation b
Externí odkaz:
http://arxiv.org/abs/2410.05500
Autor:
Zheng, Jiahao, Ren, Jinke, Xu, Peng, Yuan, Zhihao, Xu, Jie, Wang, Fangxin, Gui, Gui, Cui, Shuguang
Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic communication methods primarily focus on data reconstruction tasks, w
Externí odkaz:
http://arxiv.org/abs/2410.03459
Autor:
Yu, Bo-Wen, Liu, Bang-Gui
It is highly desirable to modify and improve the Dirac electron system of graphene for novel electronic properties and promising applications. For this purpose, we study 2D heterostructures consisting of graphene and monolayer TMDs by means of first-
Externí odkaz:
http://arxiv.org/abs/2410.02542
Time series forecasting typically needs to address non-stationary data with evolving trend and seasonal patterns. To address the non-stationarity, reversible instance normalization has been recently proposed to alleviate impacts from the trend with c
Externí odkaz:
http://arxiv.org/abs/2409.20371