Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects.

Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. The most prominent advantage of SSL is that it reduces the dependence on labeled data. Based on the pre-training and fine-tuning strategy, even a small amount of labeled data can achieve high performance.

Investigation on the Effectiveness of Augmented Reality Memory Training Game for Chinese Older Adults: A Randomized Controlled Trial.

To evaluate the effectiveness of augmented reality (AR) game based on -back training paradigm as a training tool for working memory (WM) of Chinese healthy older adults. One hundred eighteen older adults self-assessed as healthy were included in this study. Individuals were randomly divided into an intervention group ( = 57) and a control group ( = 61).

Improved inter-residue contact prediction via a hybrid generative model and dynamic loss function.

Protein contact maps represent spatial pairwise inter-residue interactions, providing a protein's translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure determination. Contact maps can also be used as a stand-alone tool for varied applications such as prediction of protein-protein interactions, structure-aware thermal stability or physicochemical properties.

Efficient robust conditional random fields.

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features.

Biologically inspired feature manifold for scene classification.

Biologically inspired feature (BIF) and its variations have been demonstrated to be effective and efficient for scene classification. It is unreasonable to measure the dissimilarity between two BIFs based on their Euclidean distance. This is because BIFs are extrinsically very high dimensional and intrinsically low dimensional, i.