Zobrazeno 1 - 10
of 14 640
pro vyhledávání: '"SUN, Jun"'
Smart contracts are the fundamental components of blockchain technology. They are programs to determine cryptocurrency transactions, and are irreversible once deployed, making it crucial for cryptocurrency investors to understand the cryptocurrency t
Externí odkaz:
http://arxiv.org/abs/2412.18484
With the prevalence of smart contracts, smart Ponzi schemes have become a common fraud on blockchain and have caused significant financial loss to cryptocurrency investors in the past few years. Despite the critical importance of detecting smart Ponz
Externí odkaz:
http://arxiv.org/abs/2412.18470
This paper investigates the volume-preserving harmonic mean curvature flow in asymptotically Schwarzschild spaces. We demonstrate the long-time existence and exponential convergence of this flow with a coordinate sphere of large radius serving as the
Externí odkaz:
http://arxiv.org/abs/2412.17024
In this paper, we will prove some rigidity theorems for blow up limits to Type II singularities of Lagrangian mean curvature flow with zero Maslov class or almost calibrated Lagrangian mean curvature flows, especially for Lagrangian translating solit
Externí odkaz:
http://arxiv.org/abs/2412.15880
Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are ro
Externí odkaz:
http://arxiv.org/abs/2412.13229
Autor:
Hong, Yunfei, Deng, Junkai, Yang, Yang, He, Ri, Zhong, Zhicheng, Ding, Xiangdong, Sun, Jun, Liu, Jefferson Zhe
Ferroelectric domain structures, separated by domain walls, often display unconventional physics and hold significant potential for applications in nano-devices. Most naturally growth domain walls are charge-neutral to avoid increased electrostatic e
Externí odkaz:
http://arxiv.org/abs/2412.10660
Autor:
Zhang, Yedi, Cai, Yufan, Zuo, Xinyue, Luan, Xiaokun, Wang, Kailong, Hou, Zhe, Zhang, Yifan, Wei, Zhiyuan, Sun, Meng, Sun, Jun, Sun, Jing, Dong, Jin Song
Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs face a cr
Externí odkaz:
http://arxiv.org/abs/2412.06512
Autor:
Hao, Yuzhou, Che, Junwei, Wang, Xiaoying, Li, Xuejie, Sun, Jun, Ding, Xiangdong, Lookman, Turab, Gao, Zhibin
Mixed anion halide-chalcogenide materials have attracted considerable attention due to their exceptional optoelectronic properties, making them promising candidates for various applications. Among these, CuBiSeCl_2 has recently been experimentally id
Externí odkaz:
http://arxiv.org/abs/2412.03976
Code generation techniques generate code snippets automatically based on the problem requirements in natural language. Recently, large language models (LLMs) achieve the SOTA performance on code generation. However, LLMs still struggle at times to ge
Externí odkaz:
http://arxiv.org/abs/2411.15587
Recent studies reveal that Large Language Models (LLMs) are susceptible to backdoor attacks, where adversaries embed hidden triggers that manipulate model responses. Existing backdoor defense methods are primarily designed for vision or classificatio
Externí odkaz:
http://arxiv.org/abs/2411.12768