Zobrazeno 1 - 10
of 941
pro vyhledávání: '"Ma Jiaqi"'
Autor:
MA Jiaqi, LI Yanyan, LIU Xiaoyan, BAI Jichao, LIU Feng, CUI Shenghui, ZHAO Linna, YANG Baowei
Publikováno v:
Zhongguo shipin weisheng zazhi, Vol 36, Iss 4, Pp 369-376 (2024)
ObjectiveTo explore the effects of sodium chloride and tryptone in the components of mannitol salt agar on the quality of mannitol salt agar， and investigate the product quality of 5 domestic brands and 1 imported brand of mannitol salt agar sold o
Externí odkaz:
https://doaj.org/article/27cca90e6f1f4211bfdda1d3f8695a55
Publikováno v:
Zhongguo shipin weisheng zazhi, Vol 36, Iss 2, Pp 147-155 (2024)
ObjectiveA TaqMan-based real-time fluorescence quantitative polymerase chain reaction （PCR） method was established to detect Clostridium perfringens （C. perfringens） in tap water samples.MethodsThe highly conserved plc gene located in the pse
Externí odkaz:
https://doaj.org/article/c2b6ed64e9334faca5f888fdbb0d68c3
This work presents an interpretable decision-making framework for autonomous vehicles that integrates traffic regulations, norms, and safety guidelines comprehensively and enables seamless adaptation to different regions. While traditional rule-based
Externí odkaz:
http://arxiv.org/abs/2410.04759
Autor:
Deng, Junwei, Li, Ting-Wei, Zhang, Shiyuan, Liu, Shixuan, Pan, Yijun, Huang, Hao, Wang, Xinhe, Hu, Pingbang, Zhang, Xingjian, Ma, Jiaqi W.
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasingly crucial role in the modern development of large-scale AI models, da
Externí odkaz:
http://arxiv.org/abs/2410.04555
Autor:
Liao, Xishun, Liu, Yifan, Kuai, Chenchen, Ma, Haoxuan, He, Yueshuai, Cao, Shangqing, Stanford, Chris, Ma, Jiaqi
Understanding human mobility patterns is crucial for urban planning, transportation management, and public health. This study tackles two primary challenges in the field: the reliance on trajectory data, which often fails to capture the semantic inte
Externí odkaz:
http://arxiv.org/abs/2410.03788
Understanding human mobility patterns has traditionally been a complex challenge in transportation modeling. Due to the difficulties in obtaining high-quality training datasets across diverse locations, conventional activity-based models and learning
Externí odkaz:
http://arxiv.org/abs/2409.17495
How can we attribute the behaviors of machine learning models to their training data? While the classic influence function sheds light on the impact of individual samples, it often fails to capture the more complex and pronounced collective influence
Externí odkaz:
http://arxiv.org/abs/2409.18153
Data attribution aims to quantify the contribution of individual training data points to the outputs of an AI model, which has been used to measure the value of training data and compensate data providers. Given the impact on financial decisions and
Externí odkaz:
http://arxiv.org/abs/2409.05657
Autor:
Stanford, Chris, Adari, Suman, Liao, Xishun, He, Yueshuai, Jiang, Qinhua, Kuai, Chenchen, Ma, Jiaqi, Tung, Emmanuel, Qian, Yinlong, Zhao, Lingyi, Zhou, Zihao, Rasheed, Zeeshan, Shafique, Khurram
Collecting real-world mobility data is challenging. It is often fraught with privacy concerns, logistical difficulties, and inherent biases. Moreover, accurately annotating anomalies in large-scale data is nearly impossible, as it demands meticulous
Externí odkaz:
http://arxiv.org/abs/2409.03024
Motion prediction plays an essential role in autonomous driving systems, enabling autonomous vehicles to achieve more accurate local-path planning and driving decisions based on predictions of the surrounding vehicles. However, existing methods negle
Externí odkaz:
http://arxiv.org/abs/2409.00904