Zobrazeno 1 - 10
of 1 146
pro vyhledávání: '"Chu, Xu"'
This review examined the current advancements in data-driven methods for analyzing flow and transport in porous media, which has various applications in energy, chemical engineering, environmental science, and beyond. Although there has been progress
Externí odkaz:
http://arxiv.org/abs/2406.19939
This project aims to advance differentiable fluid dynamics for hypersonic coupled flow over porous media, demonstrating the potential of automatic differentiation (AD)-based optimization for end-to-end solutions. Leveraging AD efficiently handles hig
Externí odkaz:
http://arxiv.org/abs/2406.19494
Driven by fundamental thermodynamic efficiency considerations, an emerging trend in the energy and propulsion systems is that the working fluid operates at a pressure above the critical pressure. Energy transport is thus accompanied by dramatic and s
Externí odkaz:
http://arxiv.org/abs/2406.04119
This study explores the dynamics of dispersed bubbly turbulent flow in a channel using interface-resolved direct numerical simulation (DNS) with an efficient Coupled Level-Set Volume-of-Fluid (CLSVOF) solver. The influence of number of bubbles (96 an
Externí odkaz:
http://arxiv.org/abs/2406.04019
Autor:
Wu, Renzhi, Chunduri, Pramod, Shah, Dristi J, Aravind, Ashmitha Julius, Payani, Ali, Chu, Xu, Arulraj, Joy, Rong, Kexin
Publikováno v:
Published on International Conference on Very Large Databases 2024
In this paper, we will present SketchQL, a video database management system (VDBMS) for retrieving video moments with a sketch-based query interface. This novel interface allows users to specify object trajectory events with simple mouse drag-and-dro
Externí odkaz:
http://arxiv.org/abs/2405.18334
Autor:
Wang, Tianlong, Jiao, Xianfeng, He, Yifan, Chen, Zhongzhi, Zhu, Yinghao, Chu, Xu, Gao, Junyi, Wang, Yasha, Ma, Liantao
Recent studies have indicated that Large Language Models (LLMs) harbor an inherent understanding of truthfulness, yet often fail to express fully and generate false statements. This gap between "knowing" and "telling" poses a challenge for ensuring t
Externí odkaz:
http://arxiv.org/abs/2406.00034
Electronic health record (EHR) data has emerged as a valuable resource for analyzing patient health status. However, the prevalence of missing data in EHR poses significant challenges to existing methods, leading to spurious correlations and suboptim
Externí odkaz:
http://arxiv.org/abs/2405.09039
Parameter-efficient fine-tuning methods, represented by LoRA, play an essential role in adapting large-scale pre-trained models to downstream tasks. However, fine-tuning LoRA-series models also faces the risk of overfitting on the training dataset, a
Externí odkaz:
http://arxiv.org/abs/2404.09610
With the increasingly powerful performances and enormous scales of pretrained models, promoting parameter efficiency in fine-tuning has become a crucial need for effective and efficient adaptation to various downstream tasks. One representative line
Externí odkaz:
http://arxiv.org/abs/2404.04316
Autor:
Yao, Yang, Wang, Xin, Zhang, Zeyang, Qin, Yijian, Zhang, Ziwei, Chu, Xu, Yang, Yuekui, Zhu, Wenwu, Mei, Hong
Large language models (LLMs) have achieved great success in many fields, and recent works have studied exploring LLMs for graph discriminative tasks such as node classification. However, the abilities of LLMs for graph generation remain unexplored in
Externí odkaz:
http://arxiv.org/abs/2403.14358