ActiveSelfHAR: Incorporating Self Training into Active Learning to Improve Cross-Subject Human Activity Recognition

Deep learning-based human activity recognition (HAR) methods have shown great promise in the applications of smart healthcare systems and wireless body sensor network (BSN). Despite their demonstrated performance in laboratory settings, the real-world implementation of such methods is still hindered by the cross-subject issue when adapting to new users. To solve this issue, we propose ActiveSelfHAR, a framework that combines active learning's benefit of sparsely acquiring data with actual labels and self- training's benefit of effectively utilizing unlabeled data to enable the deep model to adapt to the target domain, i.e., the new users. In this framework, the model trained in the last iteration or the source domain is first utilized to generate pseudo labels of the target-domain samples and construct a self-training set based on the confidence score. Second, we propose to use the spatio-temporal relationships among the samples in the non-self-training set to augment the core set selected by active learning. Finally, we combine the self-training set and the augmented core set to fine-tune the model. We demonstrate our method by comparing it with state-of-the-art methods on two IMU-based datasets and an EMG-based dataset. Our method presents similar HAR accuracies with the upper bound, i.e. fully supervised fine-tuning with less than 1\% labeled data of the target dataset and significantly improves data efficiency and time cost. Our work highlights the potential of implementing user-independent HAR methods into smart healthcare systems and BSN

Sp1 and KLF15 regulate basal transcription of the human LRP5 gene

<p>Abstract</p> <p>Background</p> <p>LRP5, a member of the low density lipoprotein receptor superfamily, regulates diverse developmental processes in embryogenesis and maintains physiological homeostasis in adult organisms. However, how the expression of human <it>LRP5 </it>gene is regulated remains unclear.</p> <p>Results</p> <p>In order to characterize the transcriptional regulation of human <it>LRP5 </it>gene, we cloned the 5' flanking region and evaluated its transcriptional activity in a luciferase reporter system. We demonstrated that both KLF15 and Sp1 binding sites between -72 bp and -53 bp contribute to the transcriptional activation of human <it>LRP5 </it>promoter. Chromatin immunoprecipitation assay demonstrated that the ubiquitous transcription factors KLF15 and Sp1 bind to this region. Using <it>Drosophila </it>SL2 cells, we showed that KLF15 and Sp1 trans-activated the <it>LRP5 </it>promoter in a manner dependent on the presence of Sp1-binding and KLF15-binding motifs.</p> <p>Conclusions</p> <p>Both KLF15 and Sp1 binding sites contribute to the basal activity of human <it>LRP5 </it>promoter. This study provides the first insight into the mechanisms by which transcription of human <it>LRP5 </it>gene is regulated.</p

Founding of the Chinese Academy of Sciences' Institute of Computing Technology

Computer science originated in the People's Republic of China in 1956 with the founding of the Chinese Academy of Sciences' Institute of Computing Technology. The Soviet Union, which played a pivotal role, gave the Chinese the opportunity to learn computer science by supplying components and describing the manufacturing process. The Soviets also helped solve key difficulties and trained workers