A Reliable Multipath Routing Protocol Based on Link Stability

Wireless NanoSensor Network (WNSN) is a new type of sensor network with broad application prospects. In view of the limited energy of nanonodes and unstable links in WNSNs, we propose a reliable multi-path routing based on link stability (RMRLS). RMRLS selects the optimal path which perfects best in the link stability evaluation model, and then selects an alternative route by the routing similarity judgment model. RMRLS uses tew paths to cope with changes in the network topology. The simulation shows that the RMRLS protocol has advantages in data packet transmission success rate and average throughput, which can improve the stability and reliability of the network

Crafting Training Degradation Distribution for the Accuracy-Generalization Trade-off in Real-World Super-Resolution

Super-resolution (SR) techniques designed for real-world applications commonly encounter two primary challenges: generalization performance and restoration accuracy. We demonstrate that when methods are trained using complex, large-range degradations to enhance generalization, a decline in accuracy is inevitable. However, since the degradation in a certain real-world applications typically exhibits a limited variation range, it becomes feasible to strike a trade-off between generalization performance and testing accuracy within this scope. In this work, we introduce a novel approach to craft training degradation distributions using a small set of reference images. Our strategy is founded upon the binned representation of the degradation space and the Fr\'echet distance between degradation distributions. Our results indicate that the proposed technique significantly improves the performance of test images while preserving generalization capabilities in real-world applications.Comment: This paper has been accepted to ICML 202

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.Comment: This article has been accepted by ECCV202

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more efficient data structures. However, these specially designed approaches do not work for most of radiance fields based methods. To resolve this issue, we introduce a general strategy to speed up the learning procedure for almost all radiance fields based methods. Our key idea is to reduce the redundancy by shooting much fewer rays in the multi-view volume rendering procedure which is the base for almost all radiance fields based methods. We find that shooting rays at pixels with dramatic color change not only significantly reduces the training burden but also barely affects the accuracy of the learned radiance fields. In addition, we also adaptively subdivide each view into a quadtree according to the average rendering error in each node in the tree, which makes us dynamically shoot more rays in more complex regions with larger rendering error. We evaluate our method with different radiance fields based methods under the widely used benchmarks. Experimental results show that our method achieves comparable accuracy to the state-of-the-art with much faster training.Comment: Accepted by lEEE Transactions on lmage Processing 2023. Project Page: https://zparquet.github.io/Fast-Learning . Code: https://github.com/zParquet/Fast-Learnin

Privacy-preserving design of graph neural networks with applications to vertical federated learning

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM). The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks. Meanwhile, transaction information is often considered highly sensitive. To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees. In this paper, we present an end-to-end GRL framework in the VFL setting called VESPER, which is built upon a general privatization scheme termed perturbed message passing (PMP) that allows the privatization of many popular graph neural architectures.Based on PMP, we discuss the strengths and weaknesses of specific design choices of concrete graph neural architectures and provide solutions and improvements for both dense and sparse graphs. Extensive empirical evaluations over both public datasets and an industry dataset demonstrate that VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets

Hetero2^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs

Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs) in handling homogeneous graphs with heterophily, little work has been conducted on investigating the heterophily properties in the context of heterogeneous graphs. To bridge this research gap, we identify the heterophily in heterogeneous graphs using metapaths and propose two practical metrics to quantitatively describe the levels of heterophily. Through in-depth investigations on several real-world heterogeneous graphs exhibiting varying levels of heterophily, we have observed that heterogeneous graph neural networks (HGNNs), which inherit many mechanisms from GNNs designed for homogeneous graphs, fail to generalize to heterogeneous graphs with heterophily or low level of homophily. To address the challenge, we present Hetero$^2$Net, a heterophily-aware HGNN that incorporates both masked metapath prediction and masked label prediction tasks to effectively and flexibly handle both homophilic and heterophilic heterogeneous graphs. We evaluate the performance of Hetero$^2$Net on five real-world heterogeneous graph benchmarks with varying levels of heterophily. The results demonstrate that Hetero$^2$Net outperforms strong baselines in the semi-supervised node classification task, providing valuable insights into effectively handling more complex heterogeneous graphs.Comment: Preprin

Association between pregnancy and pregnancy loss with COPD in Chinese women: The China Kadoorie Biobank study

Background Chronic obstructive pulmonary disease (COPD) is an inflammatory lung disease characterized by airflow blockage. Pregnancy and pregnancy loss may be related to an elevated risk of COPD, although studies have yet to report on this association. Hence, this study aims to investigate the association between pregnancy and pregnancy loss with the risk of COPD among Chinese women. Methods Data on 302,510 female participants from the China Kadoorie Biobank were utilized for this study. Multivariable logistic regression, stratified by sociodemographic and lifestyle factors, was employed to obtain the odds ratio (ORs) and 95% confidence intervals (CIs) for the association between pregnancy and pregnancy loss with COPD. Results Pregnancy loss was significantly associated with increased risk of COPD (OR 1.19, 95% CI 1.13–1.25), specifically, spontaneous (OR 1.19, 95% CI 1.11–1.29) and induced abortion (OR 1.18, 95% CI 1.12–1.25). Stillbirth, however, was not significantly associated with the risk of COPD (OR 1.09, 95% CI 0.99–1.20). Increasing number of pregnancy losses was associated with increasing risk of COPD (one pregnancy loss: OR 1.14, 95% CI 1.07–1.21, two or more pregnancy loss: OR 1.25, 95% CI 1.17–1.32, and each additional pregnancy loss: OR 1.06, 95% CI 1.03–1.09). A single pregnancy was significantly associated with reduced risk of COPD (OR 0.75, 95% CI 0.59–0.97), although each additional pregnancy was significantly associated with increased risk of COPD (OR 1.03, 95% CI 1.01–1.04). Conclusion Pregnancy loss, in particular, spontaneous and induced abortions are associated with increased risk of COPD among Chinese women. A single pregnancy, however, demonstrated protective effects

Fe-assisted epitaxial growth of 4-inch single-crystal transition-metal dichalcogenides on c-plane sapphire without miscut angle

Epitaxial growth and controllable doping of wafer-scale single-crystal transition-metal dichalcogenides (TMDCs) are two central tasks for extending Moore's law beyond silicon. However, despite considerable efforts, addressing such crucial issues simultaneously under two-dimensional (2D) confinement is yet to be realized. Here we design an ingenious epitaxial strategy to synthesize record-breaking 4-inch single-crystal Fe-doped TMDCs monolayers on industry-compatible c-plane sapphire without miscut angle. In-depth characterizations and theoretical calculations reveal that the introduction of Fe significantly decreases the formation energy of parallel steps on sapphire surfaces and contributes to the edge-nucleation of unidirectional TMDCs domains (>99%). The ultrahigh electron mobility (~86 cm2 V -1 s-1) and remarkable on/off current ratio (~108) are discovered on 4-inch single-crystal Fe-MoS2 monolayers due to the ultralow contact resistance and perfect Ohmic contact with metal electrodes. This work represents a substantial leap in terms of bridging the synthesis and doping of wafer-scale single-crystal 2D semiconductors without the need for substrate miscut, which should promote the further device downscaling and extension of Moore's law.Comment: 17 pages, 5 figure

Wear particles enhance autophagy through up-regulation of CD147 to promote osteoclastogenesis

Objective(s): The study aimed to uncover the underlying mechanism linking wear particles to osteoclast differentiation, and we explored the effect of titanium particles of different sizes on CD147 expression and autophagy in macrophages. Materials and Methods: Effects of titanium particles on CD147 and RANKL mRNA were detected by QPCR; protein level of CD147 and Beclin-1 were detected by Western blot; soluble RANKL were detected by ELISA. To determine the effect of CD147 and autophagy, KG-1a cells were transfected with siRNA-CD147 or treated with autophagy inhibitor CQ (chloroquine), and then co-cultured with different sizes of titanium particles.Results: Our results showed that 0.2-1.2 µm and 1.2-10 µm titanium particles up-regulate CD147 to activate autophagy, which increase the level of soluble RANKL to promote osteoclastogenesis. Suppression of CD147 with siRNA could diminish particle-induced autophagy and soluble RANKL expression. In addition, CQ could dramatically reduce particle-induced soluble RANKL expression. Conclusion: Our findings suggested a possible mechanism underlying wear debris-induced osteolysis and identified CD147 as a potential therapeutic target in aseptic loosening