Without loss of generality, we parameterize its surface as x(u, v) cos(2πu) sin(πv) σ(u, v) = y(u, v) = sin(2πu) sin(πv) z(u, v) cos(πv) = r1(u) × r2(v) (4) with u, v ∈ [0, 1]. In Ref. [39],...
Without loss of generality, graph smoothness is a more lenient assumption that limits the TV of the observed values of the graph signal to be small. However, graph stationary outperforms the smoothness-based method, as it has a much better prior assumption with significantly restricts the GSO. ...
Exogenous TG upregulated Bmal1 and CLOCK gene expression in macrophages and significantly increased TNF-α release. Conclusion Chronotherapy involving RSG induces TG accumulation within macrophages, resulting in alterations in circadian gene rhythms. These changes, in turn, modulate the phase of rhythmic ...
Gain- and loss-of-function studies revealed that circACTA2 alleviated VSMC inflammation by suppressing the activation of NLRP3 inflammasome. Mechanistically, cir- cACTA2 inhibited the expression of NF-κB p65 and p50 subunits and interacted with p50, which impedes the formation of the p50/p65 ...
By (48) we can assume that |k/ | ≤ 1 without loss of generality. We are now in position to formulate our main asymptotic results. Dimer–Dimer Correlations at the Rough–Smooth Boundary 1267 Proposition 2. Let the integers , k be such that + k is even and α = |k|/| | lie ...
Edges of TL are assigned a positive weight taking values either 1 or a > 0 (without loss of generality, we let 0 < a ≤ 1). First, assign to each even face a label "1" or "a" in an alternate way, as in Fig. 1. Then, we establish that the weight of an edge is given by...
The loss function for regression is set to the common smooth 𝐿1L1 loss function, i.e., 𝜎=1.0σ=1.0 as in Formula (2). Table 1. Comparison of different backbones. Compared with ResNet50 and ResNeXt50, ResNet101 and ResNeXt101 go deeper. Thus, ResNet101 and ResNeXt101 are ...
(a) Loss function, (b) Norm of gradient. We show the gradient norms of 𝐺𝐿1, 𝐺𝐿2, 𝐺𝐿1/2 and 𝑆𝐺𝐿1/2 in Figure 9b, where the oscillation of 𝐺𝐿1/2 is presented. From Figure 9, we find that the 𝑆𝐺𝐿1/2 regularizer eliminates the oscillation ...
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We assume throughout, without of loss of generality, that the input space ℐ≡{1,…,𝑛}. In addition, suppose we have a prior probability distribution 𝑝prior on ℐ that encapsulates some prior knowledge about the samples or unknown distribution. Finally, suppose we have access to a ...