Zobrazeno 1 - 10
of 3 691 994
pro vyhledávání: '"P P, Long"'
Reentrant localization has recently been observed in systems with quasi-periodic nearest-neighbor hopping, where the interplay between dimerized hopping and staggered disorder is identified as the driving mechanism. However, the robustness of reentra
Externí odkaz:
http://arxiv.org/abs/2412.13518
Autor:
Yin, Shukang, Fu, Chaoyou, Zhao, Sirui, Shen, Yunhang, Ge, Chunjiang, Yang, Yan, Long, Zuwei, Dai, Yuhan, Xu, Tong, Sun, Xing, He, Ran, Shan, Caifeng, Chen, Enhong
The success of Multimodal Large Language Models (MLLMs) in the image domain has garnered wide attention from the research community. Drawing on previous successful experiences, researchers have recently explored extending the success to the video und
Externí odkaz:
http://arxiv.org/abs/2411.19951
Autor:
Dong, Yuhao, Liu, Zuyan, Sun, Hai-Long, Yang, Jingkang, Hu, Winston, Rao, Yongming, Liu, Ziwei
Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1. Despite various efforts to improve LLM reasoning, high-quality long-
Externí odkaz:
http://arxiv.org/abs/2411.14432
Autor:
Zhao, Yilun, Long, Yitao, Jiang, Yuru, Wang, Chengye, Chen, Weiyuan, Liu, Hongjun, Zhang, Yiming, Tang, Xiangru, Zhao, Chen, Cohan, Arman
We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 e
Externí odkaz:
http://arxiv.org/abs/2411.05764
Autor:
Li, Zongyi, Hu, Shujie, Liu, Shujie, Zhou, Long, Choi, Jeongsoo, Meng, Lingwei, Guo, Xun, Li, Jinyu, Ling, Hefei, Wei, Furu
Text-to-video models have recently undergone rapid and substantial advancements. Nevertheless, due to limitations in data and computational resources, achieving efficient generation of long videos with rich motion dynamics remains a significant chall
Externí odkaz:
http://arxiv.org/abs/2410.20502
Autor:
Hu, Wenbo, Gao, Xiangjun, Li, Xiaoyu, Zhao, Sijie, Cun, Xiaodong, Zhang, Yong, Quan, Long, Shan, Ying
Estimating video depth in open-world scenarios is challenging due to the diversity of videos in appearance, content motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth seq
Externí odkaz:
http://arxiv.org/abs/2409.02095
We present Timer-XL, a generative Transformer for unified time series forecasting. To uniformly predict 1D and 2D time series, we generalize next token prediction, predominantly adopted for causal generation of 1D sequences, to multivariate next toke
Externí odkaz:
http://arxiv.org/abs/2410.04803
Autor:
Stone, Zachary, Shen, Yue, Anderson, Scott F., Bauer, Franz, Brandt, W. N., Chakraborty, Priyanka, Davis, Megan C., Fries, Logan B., Grier, Catherine J., Hall, P. B., Koekemoer, Anton M., Martínez-Aldama, Mary Loli, Long, Knox, Morrison, Sean, Ricci, Claudio, Schneider, Donald P., Temple, Matthew J., Trump, Jonathan R.
We present dynamical modeling of the broad-line region (BLR) for the highly variable AGN SDSS J141041.25+531849.0 ($z = 0.359$) using photometric and spectroscopic monitoring data from the Sloan Digital Sky Survey Reverberation Mapping project and th
Externí odkaz:
http://arxiv.org/abs/2408.04789
Autor:
Marco Floridia, Marina Giuliano, Liliana Elena Weimer, Maria Rosa Ciardi, Piergiuseppe Agostoni, Paolo Palange, Patrizia Rovere Querini, Silvia Zucco, Matteo Tosato, Aldo Lo Forte, Paolo Bonfanti, Donato Lacedonia, Emanuela Barisione, Stefano Figliozzi, Paola Andreozzi, Cecilia Damiano, Flavia Pricci, Graziano Onder, the I. S. S. Long-COVID Study Group
Publikováno v:
BMC Medicine, Vol 22, Iss 1, Pp 1-11 (2024)
Abstract Background Long-COVID symptoms remain incompletely defined due to a large heterogeneity in the populations studied, case definitions, and settings of care. The aim of this study was to assess, in patients accessing care for Long-COVID, the p
Externí odkaz:
https://doaj.org/article/27a513781e7b40259d6402f4e44219a0
Long context understanding remains challenging for large language models due to their limited context windows. This paper introduces Long Input Fine-Tuning (LIFT) for long context modeling, a novel framework that enhances LLM performance on long-cont
Externí odkaz:
http://arxiv.org/abs/2412.13626