Zobrazeno 1 - 10
of 59 777
pro vyhledávání: '"Li, XIang"'
Autor:
Xu, Shaoming, Renganathan, Arvind, Khandelwal, Ankush, Ghosh, Rahul, Li, Xiang, Liu, Licheng, Tayal, Kshitij, Harrington, Peter, Jia, Xiaowei, Jin, Zhenong, Nieber, Jonh, Kumar, Vipin
Streamflow, vital for water resource management, is governed by complex hydrological systems involving intermediate processes driven by meteorological forces. While deep learning models have achieved state-of-the-art results of streamflow prediction,
Externí odkaz:
http://arxiv.org/abs/2410.14137
We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE), for generating adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserv
Externí odkaz:
http://arxiv.org/abs/2410.13122
Transferable targeted adversarial attacks (TTAs) against deep neural networks have been proven significantly more challenging than untargeted ones, yet they remain relatively underexplored. This paper sheds new light on performing highly efficient ye
Externí odkaz:
http://arxiv.org/abs/2410.13891
Autor:
Jin, Pengfei, Shu, Peng, Kim, Sekeun, Xiao, Qing, Song, Sifan, Chen, Cheng, Liu, Tianming, Li, Xiang, Li, Quanzheng
Foundation models have become a cornerstone in deep learning, with techniques like Low-Rank Adaptation (LoRA) offering efficient fine-tuning of large models. Similarly, methods such as Retrieval-Augmented Generation (RAG), which leverage vectorized d
Externí odkaz:
http://arxiv.org/abs/2410.09908
Autor:
Pan, Yi, Jiang, Hanqi, Chen, Junhao, Li, Yiwei, Zhao, Huaqin, Zhou, Yifan, Shu, Peng, Wu, Zihao, Liu, Zhengliang, Zhu, Dajiang, Li, Xiang, Abate, Yohannes, Liu, Tianming
Neuromorphic computing has emerged as a promising energy-efficient alternative to traditional artificial intelligence, predominantly utilizing spiking neural networks (SNNs) implemented on neuromorphic hardware. Significant advancements have been mad
Externí odkaz:
http://arxiv.org/abs/2410.09674
This paper introduces a novel bionic intelligent optimisation algorithm, Octopus Inspired Optimization (OIO) algorithm, which is inspired by the neural structure of octopus, especially its hierarchical and decentralised interaction properties. By sim
Externí odkaz:
http://arxiv.org/abs/2410.07968
The DArk Matter Particle Explorer (DAMPE) is dedicated to exploring critical scientific domains including the indirect detection of dark matter, cosmic ray physics, and gamma ray astronomy. This study introduces a novel method for calibrating the Poi
Externí odkaz:
http://arxiv.org/abs/2410.07562
Autor:
Chi, Yi-Heng, Huang, Jiahui, Zhou, Ping, Feng, Hua, Li, Xiang-Dong, Markoff, Sera B., Safi-Harb, Samar, Olivera-Nieto, Laura
How black holes are formed remains an open and fundamental question in Astrophysics. Despite theoretical predictions, it lacks observations to understand whether the black hole formation experiences a supernova explosion. Here we report the discovery
Externí odkaz:
http://arxiv.org/abs/2410.06510
Instability in supernova fallback disks and its effect on the formation of ultra long period pulsars
Several pulsars with unusually long periods were discovered recently, comprising a potential population of ultra long period pulsars (ULPPs). The origin of their long periodicity is not well understood, but may be related to magnatars spun down by su
Externí odkaz:
http://arxiv.org/abs/2410.05944
We present the first complete next-to-next-to-next-to-leading-order calculation of the matching coefficients that link unpolarized flavor non-singlet parton distribution functions with lattice QCD computable correlation functions. By using this high-
Externí odkaz:
http://arxiv.org/abs/2410.05141