A Long-Tail Friendly Representation Framework for Artist and Music Similarity

The investigation of the similarity between artists and music is crucial in music retrieval and recommendation, and addressing the challenge of the long-tail phenomenon is increasingly important. This paper proposes a Long-Tail Friendly Representation Framework (LTFRF) that utilizes neural networks to model the similarity relationship. Our approach integrates music, user, metadata, and relationship data into a unified metric learning framework, and employs a meta-consistency relationship as a regular term to introduce the Multi-Relationship Loss. Compared to the Graph Neural Network (GNN), our proposed framework improves the representation performance in long-tail scenarios, which are characterized by sparse relationships between artists and music. We conduct experiments and analysis on the AllMusic dataset, and the results demonstrate that our framework provides a favorable generalization of artist and music representation. Specifically, on similar artist/music recommendation tasks, the LTFRF outperforms the baseline by 9.69%/19.42% in Hit Ratio@10, and in long-tail cases, the framework achieves 11.05%/14.14% higher than the baseline in Consistent@10

Inference of nonlinear causal effects with GWAS summary data

Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as instrumental variables (IVs). However, conventional approaches assume linear causal relations partly for simplicity and partly for the only availability of GWAS summary data. In this work, we propose a novel model {for transcriptome-wide association studies (TWAS)} to incorporate nonlinear relationships across IVs, an exposure, and an outcome, which is robust against violations of the valid IV assumptions and permits the use of GWAS summary data. We decouple the estimation of a marginal causal effect and a nonlinear transformation, where the former is estimated via sliced inverse regression and a sparse instrumental variable regression, and the latter is estimated by a ratio-adjusted inverse regression. On this ground, we propose an inferential procedure. An application of the proposed method to the ADNI gene expression data and the IGAP GWAS summary data identifies 18 causal genes associated with Alzheimer's disease, including APOE and TOMM40, in addition to 7 other genes missed by two-stage least squares considering only linear relationships. Our findings suggest that nonlinear modeling is required to unleash the power of IV regression for identifying potentially nonlinear gene-trait associations. Accompanying this paper is our Python library nl-causal(https://github.com/nl-causal/nonlinear-causal) that implements the proposed method.Comment: 36 pages, 8 figure

RainDiffusion:When Unsupervised Learning Meets Diffusion Models for Real-world Image Deraining

What will happen when unsupervised learning meets diffusion models for real-world image deraining? To answer it, we propose RainDiffusion, the first unsupervised image deraining paradigm based on diffusion models. Beyond the traditional unsupervised wisdom of image deraining, RainDiffusion introduces stable training of unpaired real-world data instead of weakly adversarial training. RainDiffusion consists of two cooperative branches: Non-diffusive Translation Branch (NTB) and Diffusive Translation Branch (DTB). NTB exploits a cycle-consistent architecture to bypass the difficulty in unpaired training of standard diffusion models by generating initial clean/rainy image pairs. DTB leverages two conditional diffusion modules to progressively refine the desired output with initial image pairs and diffusive generative prior, to obtain a better generalization ability of deraining and rain generation. Rain-Diffusion is a non adversarial training paradigm, serving as a new standard bar for real-world image deraining. Extensive experiments confirm the superiority of our RainDiffusion over un/semi-supervised methods and show its competitive advantages over fully-supervised ones.Comment: 9 page

FedVCP: A Federated-Learning-Based Cooperative Positioning Scheme for Social Internet of Vehicles

Intelligent vehicle applications, such as autonomous driving and collision avoidance, put forward a higher demand for precise positioning of vehicles. The current widely used global navigation satellite systems (GNSS) cannot meet the precision requirements of the submeter level. Due to the development of sensing techniques and vehicle-to-infrastructure (V2I) communications, some vehicles can interact with surrounding landmarks to achieve precise positioning. Existing work aims to realize the positioning correction of common vehicles by sharing the positioning data of sensor-rich vehicles. However, the privacy of trajectory data makes it difficult to collect and train data centrally. Moreover, uploading vehicle location data wastes network resources. To fill these gaps, this article proposes a vehicle cooperative positioning (CP) system based on federated learning (FedVCP), which makes full use of the potential of social Internet of Things (IoT) and collaborative edge computing (CEC) to provide high-precision positioning correction while ensuring user privacy. To the best of our knowledge, this article is the first attempt to solve the privacy of CP from a perspective of federated learning. In addition, we take the advantages of local cooperation through vehicle-to-vehicle (V2V) communications in data augmentation. For individual differences in vehicle positioning, we utilize transfer learning to eliminate the impact of such differences. Extensive experiments on real data demonstrate that our proposed model is superior to the baseline method in terms of effectiveness and convergence speed

Hesperidin inhibits the epithelial to mesenchymal transition induced by transforming growth factor-β1 in A549 cells through Smad signaling in the cytoplasm

Hesperidin, a natural compound, suppresses the epithelial-to-mesenchymal transition through the TGF-β1/ Smad signaling pathway. However, studies on the detailed effects and mechanisms of hesperidin are rare. The present study showed that, for A549 alveolar epithelial cells, the anti-proliferative effects of hesperidin occurred in a dose-dependent manner, with an IC50= 216.8 μM at 48 h. TGF-β1 was used to activate the Smad signaling pathway and induce the epithelial to mesenchymal transition in cells. Treatment with hesperidin or SB431542 was used for antagonism of Smad pathway activation. Hesperidin inhibited the increase in ɑ-SMA and Col1ɑ-1 and the decrease in E-cadherin in a dose-dependent manner from concentration of 20 μM to 60 μM, as assessed by both ELISA and Western blotting assays; however, there was no significant effect on cellular morphological alterations. Moreover, the Western blotting assay showed that, in the cytoplasm, hesperidin and SB431542 had no significant effect on the protein expression of Smad 2, 3, 4, or 7 as well as 2/3. However, 60 μM hesperidin and SB431542 significantly decreased p-Smad2/3 protein expression. From the above results, it is concluded that hesperidin can partly inhibit the epithelial to mesenchymal transition in human alveolar epithelial cells; the effect accounts for the blockage of the phosphorylation of Smad2/3 in the cytoplasm rather than a change in Smad protein production in the cytoplasm