Scalable Algorithms for Laplacian Pseudo-inverse Computation

The pseudo-inverse of a graph Laplacian matrix, denoted as $L^\dagger$, finds extensive application in various graph analysis tasks. Notable examples include the calculation of electrical closeness centrality, determination of Kemeny's constant, and evaluation of resistance distance. However, existing algorithms for computing $L^\dagger$ are often computationally expensive when dealing with large graphs. To overcome this challenge, we propose novel solutions for approximating $L^\dagger$ by establishing a connection with the inverse of a Laplacian submatrix $L_v$. This submatrix is obtained by removing the $v$-th row and column from the original Laplacian matrix $L$. The key advantage of this connection is that $L_v^{-1}$ exhibits various interesting combinatorial interpretations. We present two innovative interpretations of $L_v^{-1}$ based on spanning trees and loop-erased random walks, which allow us to develop efficient sampling algorithms. Building upon these new theoretical insights, we propose two novel algorithms for efficiently approximating both electrical closeness centrality and Kemeny's constant. We extensively evaluate the performance of our algorithms on five real-life datasets. The results demonstrate that our novel approaches significantly outperform the state-of-the-art methods by several orders of magnitude in terms of both running time and estimation errors for these two graph analysis tasks. To further illustrate the effectiveness of electrical closeness centrality and Kemeny's constant, we present two case studies that showcase the practical applications of these metrics

BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV queries. To aggregate spatial information, we design a spatial cross-attention that each BEV query extracts the spatial features from the regions of interest across camera views. For temporal information, we propose a temporal self-attention to recurrently fuse the history BEV information. Our approach achieves the new state-of-the-art 56.9\% in terms of NDS metric on the nuScenes test set, which is 9.0 points higher than previous best arts and on par with the performance of LiDAR-based baselines. We further show that BEVFormer remarkably improves the accuracy of velocity estimation and recall of objects under low visibility conditions. The code will be released at https://github.com/zhiqi-li/BEVFormer.Comment: 20 pages, 9 figure

Diversity of genotypes and pathogenicity of H9N2 avian influenza virus derived from wild bird and domestic poultry

IntroductionThe H9N2 subtype is a predominant avian influenza virus (AIV) circulating in Chinese poultry, forming various genotypes (A-W) based on gene segment origins. This study aims to investigate the genotypic distribution and pathogenic characteristics of H9N2 isolates from wild birds and domestic poultry in Yunnan Province, China.MethodsEleven H9N2 strains were isolated from fecal samples of overwintering wild birds and proximate domestic poultry in Yunnan, including four from common cranes (Grus grus), two from bar-headed geese (Anser indicus), and five from domestic poultry (Gallus gallus). Phylogenetic analysis was conducted to determine the genotypes, and representative strains were inoculated into Yunnan mallard ducks to assess pathogenicity.ResultsPhylogenetic analysis revealed that five isolates from domestic birds and one from a bar-headed goose belong to genotype S, while the remaining five isolates from wild birds belong to genotype A. These bird-derived strains possess deletions in the stalk domain of NA protein and the N166D mutation of HA protein, typical of poultry strains. Genotype S H9N2 demonstrated oropharyngeal shedding, while genotype A H9N2 exhibited cloacal shedding and high viral loads in the duodenum. Both strains caused significant pathological injuries, with genotype S inducing more severe damage to the thymus and spleen, while genotype A caused duodenal muscle layer rupture.DiscussionThese findings suggest that at least two genotypes of H9N2 are currently circulating in Yunnan, and Yunnan mallard ducks potentially act as intermediaries in interspecies transmission. These insights highlight the importance of analyzing the current epidemiological transmission characteristics of H9N2 among wild and domestic birds in China

Ranking-Incentivized Quality Preserving Content Modification

The Web is a canonical example of a competitive retrieval setting where many documents' authors consistently modify their documents to promote them in rankings. We present an automatic method for quality-preserving modification of document content -- i.e., maintaining content quality -- so that the document is ranked higher for a query by a non-disclosed ranking function whose rankings can be observed. The method replaces a passage in the document with some other passage. To select the two passages, we use a learning-to-rank approach with a bi-objective optimization criterion: rank promotion and content-quality maintenance. We used the approach as a bot in content-based ranking competitions. Analysis of the competitions demonstrates the merits of our approach with respect to human content modifications in terms of rank promotion, content-quality maintenance and relevance.Comment: 10 pages. 8 figures. 3 table