Titlebook: Complex Networks IX; Proceedings of the 9 Sean Cornelius,Kate Coronges,Alessandro Vespignani Conference proceedings 2018 Springer Internati

Vo2-Max

Stabilization of Linear Systemsrmation from both the temporal and cross-sectional network structures, which is performed by defining the loss function as a weighted sum of the supervised loss from the past dynamics and the unsupervised loss of predicting the neighborhood context in the current network. Our model is also capable o

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The Receding-Horizon State Feedbackxts upon which diffusive phenomena unfold have usually been considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work — following a data-driven approach — we compare the behaviors of classical spreading models

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The Receding-Horizon State Feedbackunclear. The present research examines the role of neuronal gain plasticity in time-series of simulated neuronal networks composed of integrate-and-fire stochastic spiking neurons and the utility of fractal methods in assessing network criticality. Simulated time-series were derived from a network m

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The Receding-Horizon State Feedbacke-by-module basis. Improvement is planned and implemented using a linear, causal, independent-problem-to-solution approach, with very little consideration given to the interconnectedness among the various components and ideas involved in these complex knowledge systems. This situation presents a nee

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https://doi.org/10.1007/978-3-319-73198-8complex networks; social networks; network science; complexity science; systems science; group decision m

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Complex Networks IX978-3-319-73198-8Series ISSN 2213-8684 Series E-ISSN 2213-8692

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