Results 91 to 100 of about 1,859 (218)
Acylindrical hyperbolicity of groups acting on trees
We provide new examples of acylindrically hyperbolic groups arising from actions on simplicial trees. In particular, we consider amalgamated products and HNN-extensions, 1-relator groups, automorphism groups of polynomial algebras, 3-manifold groups and ...
Osin, Denis +3 more
core +1 more source
The Challenge of Handling Structured Missingness in Integrated Data Sources
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source
This paper presents a computer vision (deep learning) pipeline integrating YOLOv8 and YOLOv9 for automated detection, segmentation, and analysis of rosette cellulose synthase complexes in freeze‐fracture electron microscopy images. The study explores curated dataset expansion for model improvement and highlights pipeline accuracy, speed ...
Siri Mudunuri +6 more
wiley +1 more source
Large-scale behavior of the partial duplication random graph
The following random graph model was introduced for the evolution of protein-protein interaction networks: Let $\mathcal G = (G_n)_{n=n_0, n_0+1,...}$ be a sequence of random graphs, where $G_n = (V_n, E_n)$ is a graph with $|V_n|=n$ vertices, $n=n_0,n_0+
Hermann, Felix, Pfaffelhuber, Peter
core +1 more source
A machine learning‐guided self‐driving laboratory screened over 500 nickel‐based layered double‐hydroxide catalysts for alkaline oxygen evolution. Out of the eight metals, the robot uncovered a quaternary Ni–Fe–Cr–Co catalysts requiring only 231 mV overpotential to reach 20 mA cm−2.
Nis Fisker‐Bødker +3 more
wiley +1 more source
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin +4 more
wiley +1 more source
Signed Graph Representation Learning
This thesis considers the research on signed graph representation learning in different aspects. Signed graphs model complex relations using both positive and negative edges and signed graph neural networks (SGNNs) are powerful tools to analyze signed ...
Zhang, Zeyu
core
This study reveals that sampling strategy (i.e., sampling size and approach) is a foundational prerequisite for building accurate and generalizable AI models in peptide discovery. Reaching a threshold of 7.5% of the total tetrapeptide sequence space was essential to ensure reliable predictions.
Meiru Yan +3 more
wiley +1 more source
Deep learning‐based denoising models are applied to DNA data storage systems to enhance error reduction and data fidelity. By integrating DnCNN with DNA sequence encoding methods, the study demonstrates significant improvements in image quality and correction of substitution errors, revealing a promising path toward robust and efficient DNA‐based ...
Seongjun Seo +5 more
wiley +1 more source
Adapted metrics and random walks on graphs
This thesis discusses various aspects of continuous-time simple random walks on measure weighted graphs, with a focus on behaviors related to large-scale geometric properties of the underlying graph.
Folz, Matthew Bryan
core

