Y. Zhao and G. Ciardo. Symbolic computation of strongly connected components and fair cycles using saturation. Innovations in Systems and Software Engineering, 7(2):141-150, 2011.Zhao, Y., Ciardo, G.: Symbolic computation of strongly connected components and fair cycles using saturation. Innov....
Human computation is an approach to solving problems that prove difficult using AI only, and involves the cooperation of many humans. Because human computation requires close engagement with both “human populations as users” and “human populations as driving forces,” establishing mutual trust betwee...
If it is possible to identify several strongly connected components in the dependency graph3, then the system is decoupled. It becomes then possible to go from the total order of tags implicit in physical time to a partial order imposed by the depth-first ordering of the components. This part...
connected components of an asynchronous distributed computer network. The algorithm stabilizes in O(dnΔ) rounds and every processor requires O(nlogΔ) bits, where Δ(≤ n) is an upper bound on the degree of a node, d(≤ n) is the diameter of the network, and n is the total number ...
and depends strongly on the specifics of physical error models. However the models we present here can still be used to get a meaningful insight into realistic performance. Since our model does not fix the ratio of Pauli and erasure errors, and since some correlated error structure is already...
As shown in Extended Data Figs 1g, i and 3f (top-right panel) the de-noised regression coefficients of colour and choice are strongly correlated. As a consequence, the axis of colour explains only a small fraction of the variance in the population responses (h, blue; see main text). i...
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Therefore, the graph [Math Processing Error] can be partitioned into the sets [Math Processing Error] of its maximal strongly connected components and organized into [Math Processing Error] levels, such that if there is an arc from a node in a strongly connected component [Math Processing ...
Such learning could strongly speed up convergence, and enables a preshaping of the artificial network—akin to the shaping of biological networks during development by spontaneous activity. Given diverse learning rules and task requirements, it may be questioned whether criticality is always optimal for...
identical and side vertices. Bridges and articulation vertices are edges and nodes, respectively, whose removal from a graph leads to a new graph with a greater number of connected components; degree-1 vertices are leaf nodes which, considered as source and targets, contribute equally to the ...