Zobrazeno 1 - 10
of 8 368
pro vyhledávání: '"ZHANG, LIJUN"'
Autor:
Sha, Wei E. I., Wang, Xiaoyu, Chen, Wenchao, Fu, Yuhao, Zhang, Lijun, Tian, Liang, Lin, Minshen, Jiao, Shudi, Xu, Ting, Sun, Tiange, Liu, Dongxue
Publikováno v:
Chinese Physics B 34(1): 018801, 2024
SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, pe
Externí odkaz:
http://arxiv.org/abs/2412.20009
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
Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and req
Externí odkaz:
http://arxiv.org/abs/2411.05961
In this study, we propose GITSR, an effective framework for Graph Interaction Transformer-based Scene Representation for multi-vehicle collaborative decision-making in intelligent transportation system. In the context of mixed traffic where Connected
Externí odkaz:
http://arxiv.org/abs/2411.01608
The neural network memorization problem is to study the expressive power of neural networks to interpolate a finite dataset. Although memorization is widely believed to have a close relationship with the strong generalizability of deep learning when
Externí odkaz:
http://arxiv.org/abs/2411.00372
In recent years, the study of adversarial robustness in object detection systems, particularly those based on deep neural networks (DNNs), has become a pivotal area of research. Traditional physical attacks targeting object detectors, such as adversa
Externí odkaz:
http://arxiv.org/abs/2410.10091
Domain-Incremental Learning (DIL) involves the progressive adaptation of a model to new concepts across different domains. While recent advances in pre-trained models provide a solid foundation for DIL, learning new concepts often results in the cata
Externí odkaz:
http://arxiv.org/abs/2410.00911
Publikováno v:
2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Brisbane, Australia, 2024, pp. 403-415
Randomized fault-tolerant consensus protocols with common coins are widely used in cloud computing and blockchain platforms. Due to their fundamental role, it is vital to guarantee their correctness. Threshold automata is a formal model designed for
Externí odkaz:
http://arxiv.org/abs/2409.17627
In mixed autonomy traffic environment, every decision made by an autonomous-driving car may have a great impact on the transportation system. Because of the complex interaction between vehicles, it is challenging to make decisions that can ensure bot
Externí odkaz:
http://arxiv.org/abs/2409.15105
To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent M
Externí odkaz:
http://arxiv.org/abs/2409.13783