amuch of what you do in everyday life is steered by your awareness of yourself as a social object for other people---hence,your performance for them of the roles and styles of behavior that are appropriate in the circumstances.Your "inner self" may be constrained by this awareness. 什么您...
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In other words, the actual sample cost is smaller than what can be proved rigorously. Nevertheless, the bound is quite informative about the general trends. If we choose r = λ = 1/2 for example, then Theorem 3 implies that $${N}_{\min }(\epsilon ,\delta ,\lambda ,r)\...
In other words, HDC learning is abstract and cannot be well adapted to extract low-level information from neuromorphic data. In contrast, SNN naturally models the brain’s visual systems, thus providing high classification accuracy. However, the SNN accuracy saturates with the increase in the ...
In other words, the tasks with the greater utilization are allocated first. We consider a fixed-priority assignment and we use DM priority assignment since this PA is an optimal one when the considered tasks have constrained-deadlines (∀i, Di ≤ Ti) [2]. 200 F. Fauberteau, S. Midon...
In other words, the leadless formation control problem has lower requirement on communication topologies than the leader–follower case. As far as the authors are concerned, the leaderless TVF and trajectory tracking problems of UAV swarm systems with Lipschitz nonlinear dynamics and external ...
ThesaurusAntonymsRelated WordsSynonymsLegend: Switch tonew thesaurus Adj.1. robustious- noisy and lacking in restraint or discipline; "a boisterous crowd"; "a social gathering that became rambunctious and out of hand"; "a robustious group of teenagers"; "beneath the rumbustious surface of his ...
In other words, we can regard Ψ(X, W) as the ideal feature map plus an additive error En as: Ψ(X; W) = Ψ(X; W∗) + En. (4) According to Eqn. (4) and the low-rank representation theo- ry [3], we consider Ψ(X; W∗) to be a low-rank matrix and we have: ...
In other words, all spiking neural networks (SNNs) built using this model are RNNs but not all SNNs are recurrent SNNs (RSNNs). We can cascade L spiking layers to conform a deep spiking neural network analogous to a conventional deep neural network and train it using gradient descent. ...
In other words, if the phenomenon of PPI represents just a downward scaling of the startle curve, then PPIratio cannot decrease with increasing startle sound levels. However, PPIratio does decrease with increasing startle sound levels (Fig. 1f, g), so the phenomenon of PPI must be more than...