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pro vyhledávání: '"FENG WenJie"'
Autor:
Feng Wenjie, Zhang Ye
Publikováno v:
Frontiers in Earth Science, Vol 12 (2024)
Lacustrine mixed carbonate/siliciclastic sediment is an important type of oil and gas reservoir with significant potential. Although previous studies have investigated the sedimentary characteristics of the mixed depositional system in numerous oil a
Externí odkaz:
https://doaj.org/article/5745f9dd109c4bc697ee5e33c4b1f6c8
Publikováno v:
Shock and Vibration, Vol 2023 (2023)
Red sandstone specimens with preexisting single flaw were taken as the research object in the static and dynamic loading tests. A hydraulic press was used for the uniaxial compression experiment, and SHPB was used for the impact test. The correspondi
Externí odkaz:
https://doaj.org/article/85dc3b87349a44298c21cc9274782da6
Autor:
Li, Shen, Xu, Jianqing, Wu, Jiaying, Xiong, Miao, Deng, Ailin, Ji, Jiazhen, Huang, Yuge, Feng, Wenjie, Ding, Shouhong, Hooi, Bryan
Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a privacy-preserving manner. Despite the remarkable potential of diffusion m
Externí odkaz:
http://arxiv.org/abs/2409.17576
Federated Class Incremental Learning (FCIL) is a critical yet largely underexplored issue that deals with the dynamic incorporation of new classes within federated learning (FL). Existing methods often employ generative adversarial networks (GANs) to
Externí odkaz:
http://arxiv.org/abs/2405.17457
Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviat
Externí odkaz:
http://arxiv.org/abs/2404.12130
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has explored adversarial training techniques, their improvements to defend a
Externí odkaz:
http://arxiv.org/abs/2403.18423
Autor:
Ge, Yuyao, Liu, Shenghua, Bi, Baolong, Wang, Yiwei, Mei, Lingrui, Feng, Wenjie, Chen, Lizhe, Cheng, Xueqi
Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteris
Externí odkaz:
http://arxiv.org/abs/2402.07140
Autor:
Yin Yanshu, Feng Wenjie
Publikováno v:
Open Geosciences, Vol 9, Iss 1, Pp 635-649 (2017)
In this paper, a location-based multiple point statistics method is developed to model a non-stationary reservoir. The proposed method characterizes the relationship between the sedimentary pattern and the deposit location using the relative central
Externí odkaz:
https://doaj.org/article/415651aeb50c41a2879c3a3336a2ab43
Densest Subgraph Problem (DSP) is an important primitive problem with a wide range of applications, including fraud detection, community detection and DNA motif discovery. Edge-based density is one of the most common metrics in DSP. Although a maximu
Externí odkaz:
http://arxiv.org/abs/2307.15969
Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel and general framework which un
Externí odkaz:
http://arxiv.org/abs/2305.06102