Search results for "Artificial networks"

showing 3 items of 3 documents

Weakly coupled map lattice models for multicellular patterning and collective normalization of abnormal single-cell states

2017

We present a weakly coupled map lattice model for patterning that explores the effects exerted by weakening the local dynamic rules on model biological and artificial networks composed of two-state building blocks (cells). To this end, we use two cellular automata models based on: (i) a smooth majority rule (model I) and (ii) a set of rules similar to those of Conway's Game of Life (model II). The normal and abnormal cell states evolve according with local rules that are modulated by a parameter $\kappa$. This parameter quantifies the effective weakening of the prescribed rules due to the limited coupling of each cell to its neighborhood and can be experimentally controlled by appropriate e…

0301 basic medicineNormalization (statistics)Majority ruleTime FactorsFOS: Physical sciencesAbnormal cellPattern Formation and Solitons (nlin.PS)Models BiologicalCell Physiological PhenomenaCombinatorics03 medical and health sciences0302 clinical medicineCell Behavior (q-bio.CB)Physics - Biological PhysicsGame of lifeMathematicsCellular Automata and Lattice Gases (nlin.CG)Artificial networksNonlinear Sciences - Pattern Formation and SolitonsCellular automatonMulticellular organism030104 developmental biologyBiological Physics (physics.bio-ph)030220 oncology & carcinogenesisFOS: Biological sciencesQuantitative Biology - Cell BehaviorBiological systemNonlinear Sciences - Cellular Automata and Lattice GasesCoupled map lattice
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On modelling of artificial networks arising in applications

2020

Computer scienceDistributed computingArtificial networks19th International Scientific Conference Engineering for Rural Development Proceedings
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Qualitative Comparison of Community Detection Algorithms

2011

Community detection is a very active field in complex networks analysis, consisting in identifying groups of nodes more densely interconnected relatively to the rest of the network. The existing algorithms are usually tested and compared on real-world and artificial networks, their performance being assessed through some partition similarity measure. However, artificial networks realism can be questioned, and the appropriateness of those measures is not obvious. In this study, we take advantage of recent advances concerning the characterization of community structures to tackle these questions. We first generate networks thanks to the most realistic model available to date. Their analysis r…

FOS: Computer and information sciencesPhysics - Physics and SocietyComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciences02 engineering and technologyPhysics and Society (physics.soc-ph)Similarity measure[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM][ INFO.INFO-CV ] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Complex NetworksField (computer science)Qualitative analysis020204 information systems0202 electrical engineering electronic engineering information engineeringSocial and Information Networks (cs.SI)Algorithms ComparisonArtificial networks[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]Computer Science - Social and Information Networks[ INFO.INFO-DM ] Computer Science [cs]/Discrete Mathematics [cs.DM]Complex networkPartition (database)Community Properties020201 artificial intelligence & image processingAlgorithmCommunity Detection
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