Zobrazeno 1 - 10
of 7 929
pro vyhledávání: '"Jensen, A. S."'
Autor:
Wu, Xinle, Wu, Xingjian, Zhang, Dalin, Zhang, Miao, Guo, Chenjuan, Yang, Bin, Jensen, Christian S.
Societal and industrial infrastructures and systems increasingly leverage sensors that emit correlated time series. Forecasting of future values of such time series based on recorded historical values has important benefits. Automatically designed mo
Externí odkaz:
http://arxiv.org/abs/2411.05833
Autor:
Miao, Hao, Liu, Ziqiao, Zhao, Yan, Guo, Chenjuan, Yang, Bin, Zheng, Kai, Jensen, Christian S.
The expanding instrumentation of processes throughout society with sensors yields a proliferation of time series data that may in turn enable important applications, e.g., related to transportation infrastructures or power grids. Machine-learning bas
Externí odkaz:
http://arxiv.org/abs/2410.20905
In large venues like shopping malls and airports, knowledge on the indoor populations fuels applications such as business analytics, venue management, and safety control. In this work, we provide means of modeling populations in partitions of indoor
Externí odkaz:
http://arxiv.org/abs/2410.21142
Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal dat
Externí odkaz:
http://arxiv.org/abs/2410.19192
Autor:
Li, Zhe, Qiu, Xiangfei, Chen, Peng, Wang, Yihang, Cheng, Hanyin, Shu, Yang, Hu, Jilin, Guo, Chenjuan, Zhou, Aoying, Wen, Qingsong, Jensen, Christian S., Yang, Bin
Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training a
Externí odkaz:
http://arxiv.org/abs/2410.11802
Autor:
Kühne, Nikolai L., Kitchen, Astrid H. F., Jensen, Marie S., Brøndt, Mikkel S. L., Gonzalez, Martin, Biscio, Christophe, Tan, Zheng-Hua
Automatic speech recognition (ASR) systems are known to be vulnerable to adversarial attacks. This paper addresses detection and defence against targeted white-box attacks on speech signals for ASR systems. While existing work has utilised diffusion
Externí odkaz:
http://arxiv.org/abs/2409.07936
Range-filtering approximate nearest neighbor (RFANN) search is attracting increasing attention in academia and industry. Given a set of data objects, each being a pair of a high-dimensional vector and a numeric value, an RFANN query with a vector and
Externí odkaz:
http://arxiv.org/abs/2409.02571
Autor:
Lin, Yan, Zhou, Zeyu, Liu, Yicheng, Lv, Haochen, Wen, Haomin, Li, Tianyi, Li, Yushuai, Jensen, Christian S., Guo, Shengnan, Lin, Youfang, Wan, Huaiyu
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses.
Externí odkaz:
http://arxiv.org/abs/2407.12550
The availability of massive vehicle trajectory data enables the modeling of road-network constrained movement as travel-cost distributions rather than just single-valued costs, thereby capturing the inherent uncertainty of movement and enabling impro
Externí odkaz:
http://arxiv.org/abs/2407.06881
Autor:
Zhang, Qianru, Wang, Haixin, Long, Cheng, Su, Liangcai, He, Xingwei, Chang, Jianlong, Wu, Tailin, Yin, Hongzhi, Yiu, Siu-Ming, Tian, Qi, Jensen, Christian S.
This paper focuses on the integration of generative techniques into spatial-temporal data mining, considering the significant growth and diverse nature of spatial-temporal data. With the advancements in RNNs, CNNs, and other non-generative techniques
Externí odkaz:
http://arxiv.org/abs/2405.09592