Link Prediction via Matrix Completion

Inspired by practical importance of social networks, economic networks, biological networks and so on, studies on large and complex networks have attracted a surge of attentions in the recent years. Link prediction is a fundamental issue to understand the mechanisms by which new links are added to the networks. We introduce the method of robust principal component analysis (robust PCA) into link prediction, and estimate the missing entries of the adjacency matrix. On one hand, our algorithm is based on the sparsity and low rank property of the matrix, on the other hand, it also performs very well when the network is dense. This is because a relatively dense real network is also sparse in comparison to the complete graph. According to extensive experiments on real networks from disparate fields, when the target network is connected and sufficiently dense, whatever it is weighted or unweighted, our method is demonstrated to be very effective and with prediction accuracy being considerably improved comparing with many state-of-the-art algorithms

Treated amblyopes remain deficient in spatial vision: A contrast sensitivity and external noise study

AbstractTo evaluate residual spatial vision deficits in treated amblyopia, we recruited five clinically treated amblyopes (mean age=10.6 years). Contrast sensitivity functions (CSF) in both the previously amblyopic eyes (pAE; visual acuity=0.944±0.019 MAR) and fellow eyes (pFE; visual acuity=0.936±0.021 MAR) were measured using a standard psychophysical procedure for all the subjects. The results indicated that the treated amblyopes remained deficient in spatial vision, especially at high spatial frequencies, although their Snellen visual acuity had become normal in the pAEs. To identify the mechanisms underlying spatial vision deficits of treated amblyopes, threshold vs external noise contrast (TvC) functions – the signal contrast necessary for the subject to maintain a threshold performance level in varying amounts of external noise (“TV snow”) – were measured in both eyes of four of the subjects in a sine-wave grating detection task at several spatial frequencies. Two mechanisms of amblyopia were identified: increased internal noise at low to medium spatial frequencies, and both increased internal noise and increased impact of external noise at high spatial frequencies. We suggest that, in addition to visual acuity, other tests of spatial vision (e.g., CSF, TvC) should be used to assess treatment outcomes of amblyopia therapies. Training in intermediate and high spatial frequencies may be necessary to fully recover spatial vision in amblyopia in addition to the occlusion therapy

Negative Selection by Clustering for Contrastive Learning in Human Activity Recognition

Contrastive learning has been applied to Human Activity Recognition (HAR) based on sensor data owing to its ability to achieve performance comparable to supervised learning with a large amount of unlabeled data and a small amount of labeled data. The pre-training task for contrastive learning is generally instance discrimination, which specifies that each instance belongs to a single class, but this will consider the same class of samples as negative examples. Such a pre-training task is not conducive to human activity recognition tasks, which are mainly classification tasks. To address this problem, we follow SimCLR to propose a new contrastive learning framework that negative selection by clustering in HAR, which is called ClusterCLHAR. Compared with SimCLR, it redefines the negative pairs in the contrastive loss function by using unsupervised clustering methods to generate soft labels that mask other samples of the same cluster to avoid regarding them as negative samples. We evaluate ClusterCLHAR on three benchmark datasets, USC-HAD, MotionSense, and UCI-HAR, using mean F1-score as the evaluation metric. The experiment results show that it outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.Comment: 11 pages, 5 figure