iTrace: An Implicit Trust Inference Method for Trust-aware Collaborative Filtering

The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. A collaborative filtering (CF) algorithm recommends items of interest to the target user by leveraging the votes given by other similar users. In a standard CF framework, it is assumed that the credibility of every voting user is exactly the same with respect to the target user. This assumption is not satisfied and thus may lead to misleading recommendations in many practical applications. A natural countermeasure is to design a trust-aware CF (TaCF) algorithm, which can take account of the difference in the credibilities of the voting users when performing CF. To this end, this paper presents a trust inference approach, which can predict the implicit trust of the target user on every voting user from a sparse explicit trust matrix. Then an improved CF algorithm termed iTrace is proposed, which takes advantage of both the explicit and the predicted implicit trust to provide recommendations with the CF framework. An empirical evaluation on a public dataset demonstrates that the proposed algorithm provides a significant improvement in recommendation quality in terms of mean absolute error (MAE).Comment: 6 pages, 4 figures, 1 tabl

Balancing Augmentation with Edge-Utility Filter for Signed GNNs

Signed graph neural networks (SGNNs) has recently drawn more attention as many real-world networks are signed networks containing two types of edges: positive and negative. The existence of negative edges affects the SGNN robustness on two aspects. One is the semantic imbalance as the negative edges are usually hard to obtain though they can provide potentially useful information. The other is the structural unbalance, e.g. unbalanced triangles, an indication of incompatible relationship among nodes. In this paper, we propose a balancing augmentation method to address the above two aspects for SGNNs. Firstly, the utility of each negative edge is measured by calculating its occurrence in unbalanced structures. Secondly, the original signed graph is selectively augmented with the use of (1) an edge perturbation regulator to balance the number of positive and negative edges and to determine the ratio of perturbed edges to original edges and (2) an edge utility filter to remove the negative edges with low utility to make the graph structure more balanced. Finally, a SGNN is trained on the augmented graph which effectively explores the credible relationships. A detailed theoretical analysis is also conducted to prove the effectiveness of each module. Experiments on five real-world datasets in link prediction demonstrate that our method has the advantages of effectiveness and generalization and can significantly improve the performance of SGNN backbones.Comment: 16 page

Cellular and Molecular Mechanisms Underlying Alcohol-Induced Aggressiveness of Breast Cancer

Breast cancer is a leading cause of morbidity and mortality in women. Both Epidemiological and experimental studies indicate a positive correlation between alcohol consumption and the risk of breast cancer. While alcohol exposure may promote the carcinogenesis or onset of breast cancer, it may as well enhance the progression and aggressiveness of existing mammary tumors. Recent progress in this line of research suggests that alcohol exposure is associated with invasive breast cancer and promotes the growth and metastasis of mammary tumors. There are multiple potential mechanisms involved in alcohol-stimulated progression and aggressiveness of breast cancer. Alcohol may increase the mobility of cancer cells by inducing cytoskeleton reorganization and enhancing the cancer cell invasion by causing degradation and reconstruction of the extracellular matrix (ECM). Moreover, alcohol may promote the epithelial-mesenchymal transition (EMT), a hallmark of malignancy, and impair endothelial integrity, thereby increasing the dissemination of breast cancer cells and facilitating metastasis. Furthermore, alcohol may stimulate tumor angiogenesis through the activation of cytokines and chemokines which promotes tumor growth. Additionally, alcohol may increase the cancer stem cell population which affects neoplastic cell behavior, aggressiveness, and the therapeutic response. Alcohol can be metabolized in the mammary tissues and breast cancer cells which produces reactive oxygen species (ROS), causing oxidative stress. Recent studies suggest that the epidermal growth factor receptor (EGFR) family, particularly ErbB2 (a member of this family), is involved in alcohol-mediated tumor promotion. Breast cancer cells or mammary epithelial cells over-expressing ErbB2 are more sensitive to alcohol’s tumor promoting effects. There is considerable cross-talk between oxidative stress and EGFR/ErbB2 signaling. This review further discusses how the interaction between oxidative stress and EGFR/ErbB2 signaling contributes to the cellular and molecular events associated with breast cancer aggressiveness. We also discuss the potential therapeutic approaches for cancer patients who drink alcoholic beverages

Light Multi-segment Activation for Model Compression

Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a compact student model, has been widely used for model compression. However, under the strict requirement on the resource cost, it is quite challenging to achieve comparable performance with the teacher model, essentially due to the drastically-reduced expressiveness ability of the compact student model. Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. Specifically, we propose a highly efficient multi-segment activation, called Light Multi-segment Activation (LMA), which can rapidly produce multiple linear regions with very few parameters by leveraging the statistical information. With using LMA, the compact student model is capable of achieving much better performance effectively and efficiently, than the ReLU-equipped one with same model scale. Furthermore, the proposed method is compatible with other model compression techniques, such as quantization, which means they can be used jointly for better compression performance. Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA