Zobrazeno 1 - 10
of 4 883
pro vyhledávání: '"Yantao, P."'
We conducted a series of pore-scale numerical simulations on convective flow in porous media, with a fixed Schmidt number of 400 and a wide range of Rayleigh numbers. The porous media are modeled using regularly arranged square obstacles in a Rayleig
Externí odkaz:
http://arxiv.org/abs/2409.19652
Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize
Externí odkaz:
http://arxiv.org/abs/2409.17728
Autor:
Kim, Youngeun, Fang, Jun, Zhang, Qin, Cai, Zhaowei, Shen, Yantao, Duggal, Rahul, Raychaudhuri, Dripta S., Tu, Zhuowen, Xing, Yifan, Dabeer, Onkar
The open world is inherently dynamic, characterized by ever-evolving concepts and distributions. Continual learning (CL) in this dynamic open-world environment presents a significant challenge in effectively generalizing to unseen test-time classes.
Externí odkaz:
http://arxiv.org/abs/2409.05312
Autor:
Hauschild, Johannes, Unfried, Jakob, Anand, Sajant, Andrews, Bartholomew, Bintz, Marcus, Borla, Umberto, Divic, Stefan, Drescher, Markus, Geiger, Jan, Hefel, Martin, Hémery, Kévin, Kadow, Wilhelm, Kemp, Jack, Kirchner, Nico, Liu, Vincent S., Möller, Gunnar, Parker, Daniel, Rader, Michael, Romen, Anton, Scalet, Samuel, Schoonderwoerd, Leon, Schulz, Maximilian, Soejima, Tomohiro, Thoma, Philipp, Wu, Yantao, Zechmann, Philip, Zweng, Ludwig, Mong, Roger S. K., Zaletel, Michael P., Pollmann, Frank
TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at
Externí odkaz:
http://arxiv.org/abs/2408.02010
Scanpath prediction in 360{\deg} images can help realize rapid rendering and better user interaction in Virtual/Augmented Reality applications. However, existing scanpath prediction models for 360{\deg} images execute scanpath prediction on 2D equire
Externí odkaz:
http://arxiv.org/abs/2407.10563
Autor:
Li, Yidian, Du, Xian, Cao, Yantao, Pei, Cuiying, Zhang, Mingxin, Zhao, Wenxuan, Zhai, Kaiyi, Xu, Runzhe, Liu, Zhongkai, Li, Zhiwei, Zhao, Jinkui, Li, Gang, Qi, Yanpeng, Guo, Hanjie, Chen, Yulin, Yang, Lexian
High-temperature superconductivity (HTSC) remains one of the most challenging and fascinating mysteries in condensed matter physics. Recently, superconductivity with transition temperature exceeding liquid-nitrogen temperature is discovered in La3Ni2
Externí odkaz:
http://arxiv.org/abs/2407.07501
Autor:
Zancato, Luca, Seshadri, Arjun, Dukler, Yonatan, Golatkar, Aditya, Shen, Yantao, Bowman, Benjamin, Trager, Matthew, Achille, Alessandro, Soatto, Stefano
We describe a family of architectures to support transductive inference by allowing memory to grow to a finite but a-priori unknown bound while making efficient use of finite resources for inference. Current architectures use such resources to repres
Externí odkaz:
http://arxiv.org/abs/2407.06324
Large Language Models (LLMs) have shown significant promise as copilots in various tasks. Local deployment of LLMs on edge devices is necessary when handling privacy-sensitive data or latency-sensitive tasks. The computational constraints of such dev
Externí odkaz:
http://arxiv.org/abs/2406.19227
Autor:
Chen, Wentong, Lin, Yankai, Zhou, ZhenHao, Huang, HongYun, Jia, Yantao, Cao, Zhao, Wen, Ji-Rong
In-Context Learning (ICL) is a critical capability of Large Language Models (LLMs) as it empowers them to comprehend and reason across interconnected inputs. Evaluating the ICL ability of LLMs can enhance their utilization and deepen our understandin
Externí odkaz:
http://arxiv.org/abs/2406.14955
Autor:
Chen, Tingwei, Wang, Yantao, Chen, Hanzhi, Zhao, Zijian, Li, Xinhao, Piovesan, Nicola, Zhu, Guangxu, Shi, Qingjiang
The introduction of fifth-generation (5G) radio technology has revolutionized communications, bringing unprecedented automation, capacity, connectivity, and ultra-fast, reliable communications. However, this technological leap comes with a substantia
Externí odkaz:
http://arxiv.org/abs/2406.16929