In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the l -norm usually achieve better performance than the convex case (e.g., l -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Łojasiewica (KŁ) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones.
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http://dx.doi.org/10.1109/TCYB.2020.3010960 | DOI Listing |
J Biomed Inform
January 2025
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, 02115, MA, USA; VA Boston Healthcare System, 150 S Huntington Ave, Boston, 02130, MA, USA. Electronic address:
Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. To address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features.
View Article and Find Full Text PDFUltrasonics
January 2025
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China. Electronic address:
In recent years, the widespread application of laser ultrasonic (LU) devices for obtaining internal material information has been observed. However, this approach demands a significant amount of time to acquire complete wavefield data. Hence, there is a necessity to reduce the acquisition time.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Laboratory of Adaptive Lighting Systems and Visual Processing, Technical University of Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany.
Thermopile sensor arrays provide a sufficient counterbalance between person detection and localization while preserving privacy through low resolution. The latter is especially important in the context of smart building automation applications. Current research has shown that there are two machine learning-based algorithms that are particularly prominent for general object detection: You Only Look Once (YOLOv5) and Detection Transformer (DETR).
View Article and Find Full Text PDFMicromachines (Basel)
January 2025
Research Center for Novel Computing Sensing and Intelligent Processing, Zhejiang Lab, Hangzhou 311100, China.
General matrix multiplication (GEMM) in machine learning involves massive computation and data movement, which restricts its deployment on resource-constrained devices. Although data reuse can reduce data movement during GEMM processing, current approaches fail to fully exploit its potential. This work introduces a sparse GEMM accelerator with a weight-and-output stationary (WOS) dataflow and a distributed buffer architecture.
View Article and Find Full Text PDFInt J Speech Lang Pathol
January 2025
School of Allied Health and enAble Institute, Curtin University, Perth, Western Australia, Australia.
Purpose: While there is evidence for the effectiveness of programmes targeting cyberbullying in general adolescent populations, less is known for adolescents with neurodevelopmental disorders, who are at heightened risk of involvement in cyberbullying. This scoping review aimed to identify and map the evidence in relation to managing cyberbullying among adolescents aged 10-19 with neurodevelopmental disorders.
Method: The following databases were searched: ProQuest (including dissertations and theses), PsychInfo, MEDLINE, Scopus, and Google Scholar.
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