Results 41 to 50 of about 942,096 (280)
SSEL: spike-based structural entropic learning for spiking graph neural networks
Frontiers in NeuroscienceSpiking Neural Networks (SNNs) offer transformative, event-driven neuromorphic computing with unparalleled energy efficiency, representing a third-generation AI paradigm. Extending this paradigm to graph-structured data via Spiking Graph Neural Networks (
Shuangming Yang, Yuzhu Wu, Badong Chen
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FEBS Letters, EarlyView.We present robust protocols for the preparation of supported lipid bilayers (SLBs) incorporating either Salmonella smooth LPS or outer membrane vesicles (OMVs). We use a combination of quartz crystal microbalance with dissipation (QCM‐D) and fluorescence microscopy to both characterize the SLBs of various compositions and to probe their interactions ...
Hudson P. Pace +6 more
wiley +1 more source
Graph Entropy of Some Special Chemical Graphs
مجلة بغداد للعلومChemical graph theory plays an important role in modelling molecules, especially examining physico-chemical properties of the chemical compounds. Alkanes are one of the chemical compounds which are made up of hydrogen and carbon atoms, generally known ...
B. I. Andrew, A Anuradha
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Structure Entropy and Resistor Graphs
CoRR, 2018 We propose the notion of {\it resistance of a graph} as an accompanying notion of the structure entropy to measure the force of the graph to resist cascading failure of strategic virus attacks. We show that for any connected network $G$, the resistance of $G$ is $\mathcal{R}(G)=\mathcal{H}^1(G)-\mathcal{H}^2(G)$, where $\mathcal{H}^1(G)$ and $\mathcal ...
Angsheng Li, Yicheng Pan 0001
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From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
A Graph Data Augmentation Strategy with Entropy Preservation [PDF]
, 2022 The Graph Convolutional Networks (GCN) proposed by Kipf and Welling is an effective model for semi-supervised learning, but faces the obstacle of over-smoothing, which will weaken the representation ability of GCN.
Liu, Xue, Wei, Wei, Sun, Dan
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USER: Unsupervised Structural Entropy-Based Robust Graph Neural Network
, 2023 Unsupervised/self-supervised graph neural networks (GNN) are susceptible to the inherent randomness in the input graph data, which adversely affects the model's performance in downstream tasks.
Wang, Yifei +5 more
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On Properties of Distance-Based Entropies on Fullerene Graphs
Entropy, 2019 In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph I a ( G ) , a degree-based entropy measure, the eccentric-entropy I f σ ( G ...
Modjtaba Ghorbani +4 more
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Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source
Multivariate permutation entropy, a Cartesian graph product approach [PDF]
, 2022 Entropy metrics are nonlinear measures to quantify the complexity of time series. Among them, permutation entropy is a commonly used metric due to its robustness and fast computation.
Tan, Chao +2 more
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