Zobrazeno 1 - 10
of 5 581
pro vyhledávání: '"YUAN, Quan"'
Autor:
Yuan, Quan, Zhang, Zhikun, Du, Linkang, Chen, Min, Sun, Mingyang, Gao, Yunjun, Backes, Michael, He, Shibo, Chen, Jiming
Streaming graphs are ubiquitous in daily life, such as evolving social networks and dynamic communication systems. Due to the sensitive information contained in the graph, directly sharing the streaming graphs poses significant privacy risks. Differe
Externí odkaz:
http://arxiv.org/abs/2412.11369
Predicting future bus trip chains for an existing user is of great significance for operators of public transit systems. Existing methods always treat this task as a time-series prediction problem, but the 1-dimensional time series structure cannot e
Externí odkaz:
http://arxiv.org/abs/2412.11364
In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges assoc
Externí odkaz:
http://arxiv.org/abs/2412.06871
Autor:
Xia, Yuchen, Yuan, Quan, Luo, Guiyang, Fu, Xiaoyuan, Li, Yang, Zhu, Xuanhan, Luo, Tianyou, Chen, Siheng, Li, Jinglin
Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity in collaborative perception, where agents have different and fixed perception networks, presents a major c
Externí odkaz:
http://arxiv.org/abs/2411.16799
Accurate short-term passenger flow prediction of subway stations plays a vital role in enabling subway station personnel to proactively address changes in passenger volume. Despite existing literature in this field, there is a lack of research on eff
Externí odkaz:
http://arxiv.org/abs/2410.14727
Short-term traffic volume prediction is crucial for intelligent transportation system and there are many researches focusing on this field. However, most of these existing researches concentrated on refining model architecture and ignored amount of t
Externí odkaz:
http://arxiv.org/abs/2410.14726
Autor:
Li, Yang, Yuan, Quan, Luo, Guiyang, Fu, Xiaoyuan, Zhu, Xuanhan, Yang, Yujia, Pan, Rui, Li, Jinglin
By sharing complementary perceptual information, multi-agent collaborative perception fosters a deeper understanding of the environment. Recent studies on collaborative perception mostly utilize CNNs or Transformers to learn feature representation an
Externí odkaz:
http://arxiv.org/abs/2409.07714
The isomorphism problem for digraphs is a fundamental problem in graph theory. This problem for Cayley digraphs has been extensively investigated over the last half a century. In this paper, we consider this problem for $m$-Cayley digraphs which are
Externí odkaz:
http://arxiv.org/abs/2409.00645
Autor:
Huang, Yipo, Sheng, Xiangfei, Yang, Zhichao, Yuan, Quan, Duan, Zhichao, Chen, Pengfei, Li, Leida, Lin, Weisi, Shi, Guangming
The highly abstract nature of image aesthetics perception (IAP) poses significant challenge for current multimodal large language models (MLLMs). The lack of human-annotated multi-modality aesthetic data further exacerbates this dilemma, resulting in
Externí odkaz:
http://arxiv.org/abs/2404.09624
Autor:
Pryor, Connor, Yuan, Quan, Liu, Jeremiah, Kazemi, Mehran, Ramachandran, Deepak, Bedrax-Weiss, Tania, Getoor, Lise
Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discou
Externí odkaz:
http://arxiv.org/abs/2403.17853