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Akademický článek

A Study on the Evolution of Sculpture Cultural Communication Methods and Public Art Forms Applying the Hierarchical Analysis Approach

Autor: Hu Weiguo, Liu Yaoqi

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

Based on hierarchical analysis, this paper constructs a model for evaluating the effect of sculpture culture dissemination, sculpture culture dissemination mode and evolution of public art form, and for the defects of hierarchical analysis, it constr

Externí odkaz: https://doaj.org/article/aedb08a6c70d48dfa57318f73ace38e1

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Akademický článek

Study on movement law of extremely thick strata and triggering mechanism of mine earthquakes

Autor: BAI Xianxi, CAO Anye, YANG Yao, WANG Changbin, LIU Yaoqi, ZHAO Yingchun, GUO Wenhao, GU Yingshi, WU Zhen

Publikováno v: Meitan kexue jishu, Vol 51, Iss 3, Pp 10-20 (2023)

Mine earthquake are unnatural seismic activities during mining. In the Ordos mining area in North China, the Jurassic coal seam is commonly overlain by the extremely thick cretaceous sandstone strata. It is easy to trigger strong mine earthquakes whe

Externí odkaz: https://doaj.org/article/67aac1c79f8044e7973c708f7fa878e5

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Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data

Autor: Gu, Shuhao, Zhang, Jialing, Zhou, Siyuan, Yu, Kevin, Xing, Zhaohu, Wang, Liangdong, Cao, Zhou, Jia, Jintao, Zhang, Zhuoyi, Wang, Yixuan, Hu, Zhenchong, Zhang, Bo-Wen, Li, Jijie, Liang, Dong, Zhao, Yingli, Ao, Yulong, Liu, Yaoqi, Feng, Fangxiang, Liu, Guang

Vision-Language Models (VLMs) have recently made significant progress, but the limited scale and quality of open-source instruction data hinder their performance compared to closed-source models. In this work, we address this limitation by introducin

Externí odkaz: http://arxiv.org/abs/2410.18558

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GraphTeam: Facilitating Large Language Model-based Graph Analysis via Multi-Agent Collaboration

Autor: Li, Xin, Chu, Qizhi, Chen, Yubin, Liu, Yang, Liu, Yaoqi, Yu, Zekai, Chen, Weize, Qian, Chen, Shi, Chuan, Yang, Cheng

Graphs are widely used for modeling relational data in real-world scenarios, such as social networks and urban computing. Existing LLM-based graph analysis approaches either integrate graph neural networks (GNNs) for specific machine learning tasks,

Externí odkaz: http://arxiv.org/abs/2410.18032

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Graph Fairness Learning under Distribution Shifts

Autor: Li, Yibo, Wang, Xiao, Xing, Yujie, Fan, Shaohua, Wang, Ruijia, Liu, Yaoqi, Shi, Chuan

Graph neural networks (GNNs) have achieved remarkable performance on graph-structured data. However, GNNs may inherit prejudice from the training data and make discriminatory predictions based on sensitive attributes, such as gender and race. Recentl

Externí odkaz: http://arxiv.org/abs/2401.16784

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SeisT: A foundational deep learning model for earthquake monitoring tasks

Autor: Li, Sen, Yang, Xu, Cao, Anye, Wang, Changbin, Liu, Yaoqi, Liu, Yapeng, Niu, Qiang

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing, 2024

Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake monitori

Externí odkaz: http://arxiv.org/abs/2310.01037

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