Zobrazeno 1 - 10
of 32 078
pro vyhledávání: '"A. Keogh"'
Autor:
Der, Audrey, Yeh, Chin-Chia Michael, Dai, Xin, Chen, Huiyuan, Zheng, Yan, Fan, Yujie, Zhuang, Zhongfang, Lai, Vivian, Wang, Junpeng, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Self-supervised Pretrained Models (PTMs) have demonstrated remarkable performance in computer vision and natural language processing tasks. These successes have prompted researchers to design PTMs for time series data. In our experiments, most self-s
Externí odkaz:
http://arxiv.org/abs/2408.07869
Autor:
Bonneville, Edouard F., Beyersmann, Jan, Keogh, Ruth H., Bartlett, Jonathan W., Morris, Tim P., Polverelli, Nicola, de Wreede, Liesbeth C., Putter, Hein
The Fine-Gray model for the subdistribution hazard is commonly used for estimating associations between covariates and competing risks outcomes. When there are missing values in the covariates included in a given model, researchers may wish to multip
Externí odkaz:
http://arxiv.org/abs/2405.16602
Many clinical questions involve estimating the effects of multiple treatments using observational data. When using longitudinal data, the interest is often in the effect of treatment strategies that involve sustaining treatment over time. This requir
Externí odkaz:
http://arxiv.org/abs/2405.01110
Autor:
van Geloven, Nan, Keogh, Ruth H, van Amsterdam, Wouter, Cinà, Giovanni, Krijthe, Jesse H., Peek, Niels, Luijken, Kim, Magliacane, Sara, Morzywołek, Paweł, van Ommen, Thijs, Putter, Hein, Sperrin, Matthew, Wang, Junfeng, Weir, Daniala L., Didelez, Vanessa
Prediction models are increasingly proposed for guiding treatment decisions, but most fail to address the special role of treatments, leading to inappropriate use. This paper highlights the limitations of using standard prediction models for treatmen
Externí odkaz:
http://arxiv.org/abs/2402.17366
Autor:
Head, Louise C., Negro, Giuseppe, Carenza, Livio N., Keogh, Ryan R., Gonnella, Giuseppe, Morozov, Alexander, Orlandini, Enzo, Shendruk, Tyler N., Tiribocchi, Adriano, Marenduzzo, Davide
Quasiparticles are low-energy excitations with important roles in condensed matter physics. An intriguing example is provided by Majorana fermions, quasiparticles which are identical to their antiparticles. Despite being implicated in neutrino oscill
Externí odkaz:
http://arxiv.org/abs/2402.16149
Liquid metal batteries (LMBs) are a promising grid-scale storage device however, the scalability of this technology and its electrochemical performance is limited by mass transport overpotentials. In this work, a numerical model of a three-layer LMB
Externí odkaz:
http://arxiv.org/abs/2312.07897
Autor:
Yeh, Chin-Chia Michael, Chen, Huiyuan, Fan, Yujie, Dai, Xin, Zheng, Yan, Lai, Vivian, Wang, Junpeng, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Time series classification is a widely studied problem in the field of time series data mining. Previous research has predominantly focused on scenarios where relevant or foreground subsequences have already been extracted, with each subsequence corr
Externí odkaz:
http://arxiv.org/abs/2311.02561
Autor:
Der, Audrey, Yeh, Chin-Chia Michael, Zheng, Yan, Wang, Junpeng, Chen, Huiyuan, Zhuang, Zhongfang, Wang, Liang, Zhang, Wei, Keogh, Eamonn
Publishing and sharing data is crucial for the data mining community, allowing collaboration and driving open innovation. However, many researchers cannot release their data due to privacy regulations or fear of leaking confidential business informat
Externí odkaz:
http://arxiv.org/abs/2311.02563
Autor:
Yeh, Chin-Chia Michael, Zheng, Yan, Pan, Menghai, Chen, Huiyuan, Zhuang, Zhongfang, Wang, Junpeng, Wang, Liang, Zhang, Wei, Phillips, Jeff M., Keogh, Eamonn
Time series discords are a useful primitive for time series anomaly detection, and the matrix profile is capable of capturing discord effectively. There exist many research efforts to improve the scalability of discord discovery with respect to the l
Externí odkaz:
http://arxiv.org/abs/2311.03393
Autor:
Seaman, Shaun R, Keogh, Ruth H
Marginal structural models (MSMs) are often used to estimate causal effects of treatments on survival time outcomes from observational data when time-dependent confounding may be present. They can be fitted using, e.g., inverse probability of treatme
Externí odkaz:
http://arxiv.org/abs/2309.05025