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A Rank-Two Case of Local-Global Compatibility for $l = p$

Autor: Yang, Yuji

We prove the classical $l = p$ local-global compatibility conjecture for certain regular algebraic cuspidal automorphic representations of weight 0 for GL$_2$ over CM fields. Using an automorphy lifting theorem, we show that if the automorphic side c

Externí odkaz: http://arxiv.org/abs/2407.00288

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GIC: Gaussian-Informed Continuum for Physical Property Identification and Simulation

Autor: Cai, Junhao, Yang, Yuji, Yuan, Weihao, He, Yisheng, Dong, Zilong, Bo, Liefeng, Cheng, Hui, Chen, Qifeng

This paper studies the problem of estimating physical properties (system identification) through visual observations. To facilitate geometry-aware guidance in physical property estimation, we introduce a novel hybrid framework that leverages 3D Gauss

Externí odkaz: http://arxiv.org/abs/2406.14927

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M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation

Autor: Huai, Zepeng, Yang, Yuji, Zhang, Mengdi, Zhang, Zhongyi, Li, Yichun, Wu, Wei

Publikováno v: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023)

Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products can be described by a piece of text, especially when cold-start users o

Externí odkaz: http://arxiv.org/abs/2304.07911

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Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation

Autor: Chi, Huixuan, Xu, Hao, Fu, Hao, Liu, Mengya, Zhang, Mengdi, Yang, Yuji, Hao, Qinfen, Wu, Wei

Modeling the evolution of user preference is essential in recommender systems. Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majority of which focus on user's stable long-term preference. However, in re

Externí odkaz: http://arxiv.org/abs/2208.00593

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Ensemble Multi-Relational Graph Neural Networks

Autor: Wang, Yuling, Xu, Hao, Yu, Yanhua, Zhang, Mengdi, Li, Zhenhao, Yang, Yuji, Wu, Wei

It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is

Externí odkaz: http://arxiv.org/abs/2205.12076

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An Ordinary Rank-Two Case of Local-Global Compatibility for Automorphic Representations of Arbitrary Weight Over CM Fields

Autor: Yang, Yuji

We prove a rank-two potential automorphy theorem for mod $l$ representations satisfying an ordinary condition. Combined with an ordinary automorphy lifting theorem, we prove a rank-two, $p \ne l$ case of local-global compatibility for regular algebra

Externí odkaz: http://arxiv.org/abs/2111.00318

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