Results 91 to 100 of about 5,579,561 (370)
Deformable Rapidly-Exploring Random Trees
Robotics: Science and Systems, 2017 In this paper, using the Hypercube Diagonal Experiment we first investigate the convergence rates of samplingbased path-planning algorithms in terms of the dimensionnality of the search space.
Florian Hauer, P. Tsiotras
semanticscholar +1 more source
Journal of Computing and Information Technology, 2023 With the advancement of the university information process, more and more application systems are running on the campus network, and the information system becomes larger and more complex. With the rapid growth of network users and the popularization and
Li Yin, Yijun Chen
doaj +1 more source
Sharp phase transition for the random-cluster and Potts models via decision trees [PDF]
Annals of Mathematics, 2017 We prove an inequality on decision trees on monotonic measures which generalizes the OSSS inequality on product spaces. As an application, we use this inequality to prove a number of new results on lattice spin models and their random-cluster ...
H. Duminil-Copin +2 more
semanticscholar +1 more source
Extremal properties of random trees [PDF]
Physical Review E, 2001 4 pages ...
Eli Ben-Naim +3 more
openaire +3 more sources
Machine Learning‐Guided Discovery of Factors Governing Deformation Twinning in Mg–Y Alloys
Advanced Engineering Materials, EarlyView.This study uses interpretable machine learning to identify key microstructural and processing parameters related to twinning in magnesium‐yttrium (Mg–Y) alloys. It is identified that using only grain size, grain orientation, and total applied strain, grains can be classified with 84% accuracy based on whether the grain contains a twin.
Peter Mastracco +8 more
wiley +1 more source
Non-crossing trees revisited: cutting down and spanning subtrees [PDF]
Discrete Mathematics & Theoretical Computer Science, 2003 Here we consider two parameters for random non-crossing trees: $\textit{(i)}$ the number of random cuts to destroy a size-$n$ non-crossing tree and $\textit{(ii)}$ the spanning subtree-size of $p$ randomly chosen nodes in a size-$n$ non-crossing tree ...
Alois Panholzer
doaj +1 more source
IEEE Access, 2022 We propose a new ML model called Topological Forest that contains an ensemble of decision trees. Unlike a vanilla Random Forest, Topological Forest has a special training process that selects a smaller number of decision trees on a topological graph ...
Murat Ali Bayir +3 more
doaj +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
Advanced Engineering Materials, EarlyView.This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani +4 more
wiley +1 more source
Degree distribution of random Apollonian network structures and Boltzmann sampling [PDF]
Discrete Mathematics & Theoretical Computer Science, 2007 Random Apollonian networks have been recently introduced for representing real graphs. In this paper we study a modified version: random Apollonian network structures (RANS), which preserve the interesting properties of real graphs and can be handled ...
Alexis Darrasse, Michèle Soria
doaj +1 more source
Embedding small digraphs and permutations in binary trees and split trees [PDF]
, 2019 We investigate the number of permutations that occur in random labellings of trees. This is a generalisation of the number of subpermutations occurring in a random permutation. It also generalises some recent results on the number of inversions in randomly labelled trees.
arxiv +1 more source