307 research outputs found
Fast Graph Condensation with Structure-based Neural Tangent Kernel
The rapid development of Internet technology has given rise to a vast amount
of graph-structured data. Graph Neural Networks (GNNs), as an effective method
for various graph mining tasks, incurs substantial computational resource costs
when dealing with large-scale graph data. A data-centric manner solution is
proposed to condense the large graph dataset into a smaller one without
sacrificing the predictive performance of GNNs. However, existing efforts
condense graph-structured data through a computational intensive bi-level
optimization architecture also suffer from massive computation costs. In this
paper, we propose reforming the graph condensation problem as a Kernel Ridge
Regression (KRR) task instead of iteratively training GNNs in the inner loop of
bi-level optimization. More specifically, We propose a novel dataset
condensation framework (GC-SNTK) for graph-structured data, where a
Structure-based Neural Tangent Kernel (SNTK) is developed to capture the
topology of graph and serves as the kernel function in KRR paradigm.
Comprehensive experiments demonstrate the effectiveness of our proposed model
in accelerating graph condensation while maintaining high prediction
performance. The source code is available on
https://github.com/WANGLin0126/GCSNTK.Comment: 10 pages, 6 figures, 5 table
How hypoxia affects microbiota metabolism in mice
ObjectiveTo investigate the relationship between gut microbiota and the fecal metabolites of hypoxic environments in mice.MethodsHigh-fat diet-induced obese mice (n = 20) and normal diet-fed mice (n = 20) were randomly divided into four groups: high altitude obese group (HOB), high altitude normal weight group (HN), low altitude obese group LOB (LOB), and low altitude normal weight group (LN). Fecal samples from each group were 16S rRNA gene sequenced, and five samples from each of the four groups above were selected for non-targeted fecal metabolomics analysis using liquid chromatography-mass spectrometry. The relationship between gut microbiota and fecal metabolites was analyzed using SIMCA 14.1, MetaboAnalyst 5.0 and R 4.1.11.Results(A) Body weight was significantly lower in the hypoxic obesity group than in the normoxic obesity group. (B) Differences in α-diversity and β-diversity were found in the fecal gut microbiota of mice of different body weights and altitude, and the diversity of gut microbiota was higher in the normal group than in the obese group; the results of the comparison between the two groups showed that Faecalibaculum, Romboutsia, Lactobacillus, and A2 were associated with obesity; Romboutsia was associated with hypoxia. (C) The metabolic profiles of fecal metabolites differed between groups: gut microbiota were associated with nucleotide and amino acid metabolism in the same body groups, while gut microbiota were associated with lipid and amino acid metabolism in the same oxygen concentration groups.Conclusion(a) Gut microbiota diversity was reduced in obese groups. Romboutsia was the dominant microbiota in the hypoxia group. (b) Gut microbiota were associated with nucleotide and amino acid metabolism in the same body weight groups, while they were associated with lipid and amino acid metabolism in the same altitude groups
An Expression Tree Decoding Strategy for Mathematical Equation Generation
Generating mathematical equations from natural language requires an accurate
understanding of the relations among math expressions. Existing approaches can
be broadly categorized into token-level and expression-level generation. The
former treats equations as a mathematical language, sequentially generating
math tokens. Expression-level methods generate each expression one by one.
However, each expression represents a solving step, and there naturally exist
parallel or dependent relations between these steps, which are ignored by
current sequential methods. Therefore, we integrate tree structure into the
expression-level generation and advocate an expression tree decoding strategy.
To generate a tree with expression as its node, we employ a layer-wise parallel
decoding strategy: we decode multiple independent expressions (leaf nodes) in
parallel at each layer and repeat parallel decoding layer by layer to
sequentially generate these parent node expressions that depend on others.
Besides, a bipartite matching algorithm is adopted to align multiple
predictions with annotations for each layer. Experiments show our method
outperforms other baselines, especially for these equations with complex
structures.Comment: Accepted to EMNLP-2023, camera-ready versio
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