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Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network

Autor: Xie, Jiayi, Li, Hongfeng, Cheng, Jin, Cai, Qingrui, Tan, Hanbo, Zu, Lingyun, Qu, Xiaobo, Han, Hongbin

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory,

Externí odkaz: http://arxiv.org/abs/2401.12435

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UnifiedSSR: A Unified Framework of Sequential Search and Recommendation

Autor: Xie, Jiayi, Liu, Shang, Cong, Gao, Chen, Zhenzhong

In this work, we propose a Unified framework of Sequential Search and Recommendation (UnifiedSSR) for joint learning of user behavior history in both search and recommendation scenarios. Specifically, we consider user-interacted products in the recom

Externí odkaz: http://arxiv.org/abs/2310.13921

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DarkVision: A Benchmark for Low-light Image/Video Perception

Autor: Zhang, Bo, Guo, Yuchen, Yang, Runzhao, Zhang, Zhihong, Xie, Jiayi, Suo, Jinli, Dai, Qionghai

Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which de

Externí odkaz: http://arxiv.org/abs/2301.06269

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Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation

Autor: Xie, Jiayi, Chen, Zhenzhong

Next point-of-interest (POI) recommendation is a critical task in location-based social networks, yet remains challenging due to a high degree of variation and personalization exhibited in user movements. In this work, we explore the latent hierarchi

Externí odkaz: http://arxiv.org/abs/2209.01559

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Deep Causal Reasoning for Recommendations

Autor: Zhu, Yaochen, Yi, Jing, Xie, Jiayi, Chen, Zhenzhong

Traditional recommender systems aim to estimate a user's rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead

Externí odkaz: http://arxiv.org/abs/2201.02088

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