Results 31 to 40 of about 116,505 (190)

Perfect edge domination : hard and solvable cases [PDF]

open access: yes, 2017
Let G be an undirected graph. An edge of Gdominates itself and all edges adjacent to it. A subset E′ of edges of G is an edge dominating set of G, if every edge of the graph is dominated by some edge of E′. We say that E′ is a perfect edge dominating set
Lin, Min Chih   +3 more
core   +3 more sources

Local Search and the Evolution of World Models

open access: yesTopics in Cognitive Science, EarlyView., 2023
Abstract An open question regarding how people develop their models of the world is how new candidates are generated for consideration out of infinitely many possibilities. We discuss the role that evolutionary mechanisms play in this process. Specifically, we argue that when it comes to developing a global world model, innovation is necessarily ...
Neil R. Bramley   +3 more
wiley   +1 more source

An Introduction to Predictive Processing Models of Perception and Decision‐Making

open access: yesTopics in Cognitive Science, EarlyView., 2023
Abstract The predictive processing framework includes a broad set of ideas, which might be articulated and developed in a variety of ways, concerning how the brain may leverage predictive models when implementing perception, cognition, decision‐making, and motor control.
Mark Sprevak, Ryan Smith
wiley   +1 more source

Design of Virtual Network Mapping Algorithm Based on K-Best Perfect Matchings of Bipartite Graph

open access: yesDianxin kexue, 2014
To improve the feasibility of virtual node mapping,grounded on feasibility test theorem and node rank indicators used to measure node availability,the virtual network mapping iterative algorithm based on K-best perfect matchings of bipartite graph was ...
Jianjun Yu, Chunming Wu
doaj   +2 more sources

Spin‐Split Edge States in Metal‐Supported Graphene Nanoislands Obtained by CVD

open access: yesAdvanced Materials, EarlyView.
Combining STM measurements and ab‐initio calculations, we show that zig‐zag edges in graphene nanoislands grown on Ni(111) by CVD retrieve their spin‐polarized edge states after intercalation of a few monolayers of Au. ABSTRACT Spin‐split states localized on zigzag edges have been predicted for different free‐standing graphene nanostructures.
Michele Gastaldo   +6 more
wiley   +1 more source

Imperfection in Semiconductors Leading to High Performance Devices

open access: yesAdvanced Science, EarlyView.
Crystalline perfection is typically pursued in semiconductors to enhance device performance. However, through modeling and experimental work, we show that defects can be strategically employed in a specific detection regime to increase sensitivity to extreme values. GaN diodes are demonstrated to effectively detect high‐energy proton beams at fluxes as
Jean‐Yves Duboz   +8 more
wiley   +1 more source

On Perfect Matchings and tilings in uniform Hypergraphs

open access: yes, 2018
In this paper we study some variants of Dirac-type problems in hypergraphs. First, we show that for $k\ge 3$, if $H$ is a $k$-graph on $n\in k\mathbb N$ vertices with independence number at most $n/p$ and minimum codegree at least $(1/p+o(1))n$, where $p$
Han, Jie
core   +1 more source

Graph‐Theory Approach to Element Miscibility and Alloy Design

open access: yesAdvanced Science, EarlyView.
Graph and network theory enables pathway toward complex multiscale interactions between different elements for alloy design or interface engineering. Utilizing element's inherent properties and preferential interactivity, favorable mixed material formation, solubility and miscibility can be predicted.
Andrew Martin   +6 more
wiley   +1 more source

Resilient degree sequences with respect to Hamilton cycles and matchings in random graphs

open access: yes, 2019
P\'osa's theorem states that any graph $G$ whose degree sequence $d_1 \le \ldots \le d_n$ satisfies $d_i \ge i+1$ for all $i < n/2$ has a Hamilton cycle. This degree condition is best possible.
Condon, Padraig   +4 more
core   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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