This is a continuation of the study, begun by Ceccherini-Silberstein and Woess (2009) [5], of context-free pairs of groups and the related context-free graphs in the sense of Muller and Schupp (1985) [22]. The graphs under consideration are Schreier graphs of a subgroup of some finitely generated group, and context-freeness relates to a tree-like structure of those graphs. Instead of the cones of Muller and Schupp (1985) [22] (connected components resulting from deletion of finite balls with respect to the graph metric), a more general approach to context-free graphs is proposed via tree sets consisting of cuts of the graph, and associated structure trees. The existence of tree sets with certain "good" properties is studied. With a tree set, a natural context-free grammar is associated. These investigations of the structure of context free pairs, resp. graphs are then applied to study random walk asymptotics via complex analysis. In particular, a complete proof of the local limit theorem for return probabilities on any virtually free group is given, as well as on Schreier graphs of a finitely generated subgoup of a free group. This extends, respectively completes, the significant work of Lalley (1993, 2001) [18,20].
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3257889 | PMC |
http://dx.doi.org/10.1016/j.disc.2011.07.026 | DOI Listing |
J Chem Inf Model
January 2025
Biostatistics and Bioinformatics Unit, IMDEA Food, E28049 Madrid, Spain.
Functional groups are widely used in organic chemistry, because they provide a rationale to analyze physicochemical and reactivity properties. In medicinal chemistry, they are the basis for analyzing ligand-biomacromolecule interactions. Ertl's algorithm is an approach to extract functional groups in arbitrary organic molecules that does not depend on predefined libraries of functional groups.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Dept. of Computer Science & Engineering and Convergence Engineering for Intelligent Drone, XR Research Center, Sejong University, Seoul, Republic of Korea. Electronic address:
Flooding presents substantial dangers to human lives and infrastructure, underscoring the need to map flood-prone areas to implement effective mitigation measures precisely. Although machine learning algorithms have made great strides, their accuracy in flood susceptibility mapping (FSM) remains limited due to data dependence, interpretability, and explainability issues, overfitting, generalization difficulties, and hyperparameter tuning. This study suggests combining the Decision Tree (DT) algorithm with advanced, math-based metaheuristic optimization algorithms to address these limitations.
View Article and Find Full Text PDFPlant Dis
January 2025
Hebei Academy of Agricultural and Forestry Sciences, Plant Protection Institute, 437 Dongguan Street, Baoding, Hebei, China, 071000.
Strawberry () is an important economic crop in Hebei, China. In May 2023, root rot was observed in strawberry plantations (cultivar 'Benihoppe') in Shijiazhuang (37°57'23″N, 115°16'34″E), Hebei, China. The incidence of the disease reached up to 30% in the field.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia.
Objective: We aimed to develop a highly interpretable and effective, machine-learning based risk prediction algorithm to predict in-hospital mortality, intubation and adverse cardiovascular events in patients hospitalised with COVID-19 in Australia (AUS-COVID Score).
Materials And Methods: This prospective study across 21 hospitals included 1714 consecutive patients aged ≥ 18 in their index hospitalization with COVID-19. The dataset was separated into training (80%) and test sets (20%).
JCO Clin Cancer Inform
January 2025
Machine Learning Department, H. Lee Moffit Cancer Center and Research Institute, Tampa, FL.
Purpose: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evaluated whether an auxiliary data set could improve prediction performance.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!