38 research outputs found
Integrating Social Circles and Network Representation Learning for Item Recommendation
With the increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize trust relationships of users to improve the performance of recommendation algorithms. However, most of existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates social circles and network representation learning for item recommendation. Specifically, we first infer domain-specific social trust circles based on original usersâ rating information and social network information. Next, we adopt network representation technique to embed domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships into domain-specific matrix factorization model to learn latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms traditional social-network-based recommendation algorithms
Proanthocyanidins-induced horizontal arrangement poly(vinyl alcohol)/graphene composites with enhanced mechanical properties
A green approach is employed to prepare mechanically enhanced composites by adding noncovalently proanthocyanidin (PC)âmodified graphene (PCârGO) into poly(vinyl alcohol) (PVA). Ascorbic acid (AA) is used as the reducing agent, and PC is used as a dispersant to synthesize lowâdefect and fully dispersed graphene. After static treatment, the PCârGO sheets in the composite form a horizontally arranged structure. Compared with neat PVA, the Young's modulus of the grapheneâmodified composites is significantly enhanced by approximately 79.3% with incorporation of 0.9 wt% PCârGO. The composites incorporated with GO or AAârGO (without PC) have randomly distributed GO structures and apparent rGO agglomeration, resulting in a weaker mechanical property. The dispersibility, degree of defects, distribution state of graphene, and interactions with the polymer matrix are directly related to the final mechanical performance. This new approach to mechanically enhance grapheneâembedded PVA composites provides the possibility for largeâscale production of grapheneâreinforced composite materials
Eyes-Closed Resting EEG Predicts the Learning of Alpha Down-Regulation in Neurofeedback Training
Neurofeedback training, which enables the trainee to learn self-control of the EEG activity of interest based on online feedback, has demonstrated benefits on cognitive and behavioral performance. Nevertheless, as a core mechanism of neurofeedback, learning of EEG regulation (i.e., EEG learning) has not been well understood. Moreover, a substantial number of non-learners who fail to achieve successful EEG learning have often been reported. This study investigated the EEG learning in alpha down-regulation neurofeedback, aiming to better understand the alpha learning and to early predict learner/non-learner. Twenty-nine participants received neurofeedback training to down-regulate alpha in two days, while eight of them were identified as non-learners who failed to reduce their alpha within sessions. Through a stepwise linear discriminant analysis, a prediction model was built based on participantâs eyes-closed resting EEG activities in broad frequency bands including lower alpha, theta, sigma and beta 1 measured before training, which was validated in predicting learners/non-learners. The findings would assist in the early identification of the individuals who would not likely reduce their alpha during neurofeedback
Dense Feature Aggregation and Pruning for RGBT Tracking
How to perform effective information fusion of different modalities is a core
factor in boosting the performance of RGBT tracking. This paper presents a
novel deep fusion algorithm based on the representations from an end-to-end
trained convolutional neural network. To deploy the complementarity of features
of all layers, we propose a recursive strategy to densely aggregate these
features that yield robust representations of target objects in each modality.
In different modalities, we propose to prune the densely aggregated features of
all modalities in a collaborative way. In a specific, we employ the operations
of global average pooling and weighted random selection to perform channel
scoring and selection, which could remove redundant and noisy features to
achieve more robust feature representation. Experimental results on two RGBT
tracking benchmark datasets suggest that our tracker achieves clear
state-of-the-art against other RGB and RGBT tracking methods.Comment: arXiv admin note: text overlap with arXiv:1811.0985