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Online ensemble model compression for nonstationary data stream learning.
Neural Netw
January 2025
School of Computer Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom.
Learning from data streams that emerge from nonstationary environments has many real-world applications and poses various challenges. A key characteristic of such a task is the varying nature of the underlying data distributions over time (concept drifts). However, the most common type of data stream learning approach are ensemble approaches, which involve the training of multiple base learners.
View Article and Find Full Text PDFAnimals survive in dynamic environments changing at arbitrary timescales, but such data distribution shifts are a challenge to neural networks. To adapt to change, neural systems may change a large number of parameters, which is a slow process involving forgetting past information. In contrast, animals leverage distribution changes to segment their stream of experience into tasks and associate them with internal task abstracts.
View Article and Find Full Text PDFExploring experiences of dementia post-diagnosis support and ideas for improving practice: A co-produced study.
Dementia (London)
January 2025
Members of the Forget-Me-Not Research Group, UK.
It takes time to adjust to a diagnosis of dementia. Post-diagnosis support has an important part to play in navigating this transition. However, it is often scarce and variable according to location.
View Article and Find Full Text PDFPsychometric properties of the Arabic version of the Forgotten Joint Score usage in total hip arthroplasty.
J Orthop Surg Res
January 2025
College of Medicine, King Saud University, Riyadh, Saudi Arabia.
Background: The ultimate goal of arthroplasty is thought to be the ability to "forget" a joint implant in daily activities. The Forgotten Joint Score (FJS-12), a score system that evaluates how much patients have been able to forget their hip or knee prosthesis, was recently published. It is based on a self-administered questionnaire that consists of 12 items.
View Article and Find Full Text PDFRecursive hierarchical parametric identification of Wiener-Hammerstein systems based on initial value optimization.
ISA Trans
January 2025
Institute of Artificial Intelligence and Future Networks, Beijing Normal University at Zhuhai, Zhuhai, China; BNU-HKBU United International College Tangjiawan, Rd. JinTong 2000#, Zhuhai, China. Electronic address:
In this paper, a novel recursive hierarchical parametric identification method based on initial value optimization is proposed for Wiener-Hammerstein systems subject to stochastic measurement noise. By transforming the traditional Wiener-Hammerstein system model into a generalized form, the system model parameters are uniquely expressed for estimation. To avoid cross-coupling between estimating block-oriented model parameters, a hierarchical identification algorithm is presented by dividing the parameter vector into two subvectors containing the coupled and uncoupled terms for estimation, respectively.
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