382 research outputs found
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs
Recent studies have shown that attackers can catastrophically reduce the
performance of GNNs by maliciously modifying the graph structure or node
features on the graph. Adversarial training, which has been shown to be one of
the most effective defense mechanisms against adversarial attacks in computer
vision, holds great promise for enhancing the robustness of GNNs. There is
limited research on defending against attacks by performing adversarial
training on graphs, and it is crucial to delve deeper into this approach to
optimize its effectiveness. Therefore, based on robust adversarial training on
graphs, we propose a hierarchical constraint refinement framework (HC-Ref) that
enhances the anti-perturbation capabilities of GNNs and downstream classifiers
separately, ultimately leading to improved robustness. We propose corresponding
adversarial regularization terms that are conducive to adaptively narrowing the
domain gap between the normal part and the perturbation part according to the
characteristics of different layers, promoting the smoothness of the predicted
distribution of both parts. Moreover, existing research on graph robust
adversarial training primarily concentrates on training from the standpoint of
node feature perturbations and seldom takes into account alterations in the
graph structure. This limitation makes it challenging to prevent attacks based
on topological changes in the graph. This paper generates adversarial examples
by utilizing graph structure perturbations, offering an effective approach to
defend against attack methods that are based on topological changes. Extensive
experiments on two real-world graph benchmarks show that HC-Ref successfully
resists various attacks and has better node classification performance compared
to several baseline methods
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
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
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
MicroGlam: Microscopic Skin Image Dataset with Cosmetics
In this paper, we present a cosmetic-specific skin image dataset. It consists
of skin images from patches ( skin patches each from participants)
of size under three cosmetic products (i.e., foundation, blusher,
and highlighter). We designed a novel capturing device inspired by Light Stage.
Using the device, we captured over images of each skin patch under
diverse lighting conditions in seconds. We repeated the process for the
same skin patch under three cosmetic products. Finally, we demonstrate the
viability of the dataset with an image-to-image translation-based pipeline for
cosmetic rendering and compared our data-driven approach to an existing
cosmetic rendering method.Comment: Project Page: https://github.com/tobyclh/MicroGla
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