Abstract In the (special) smoothing spline problem one considers a variational problem with a quadratic data fidelity penalty and Laplacian regularization. Higher order regularity can be obtained via replacing the Laplacian regulariser with a poly-Laplacian regulariser. The methodology is readily adapted to graphs and here we consider graph poly-Laplacian regularization in a fully supervised, non-parametric, noise corrupted, regression problem. In particular, given a dataset$$\{x_i\}_{i=1}^n$$ and a set of noisy labels$$\{y_i\}_{i=1}^n\subset \mathbb {R}$$ we let$$u_n{:}\{x_i\}_{i=1}^n\rightarrow \mathbb {R}$$ be the minimizer of an energy which consists of a data fidelity term and an appropriately scaled graph poly-Laplacian term. When$$y_i = g(x_i)+\xi _i$$ , for iid noise$$\xi _i$$ , and using the geometric random graph, we identify (with high probability) the rate of convergence of$$u_n$$ togin the large data limit$$n\rightarrow \infty $$ . Furthermore, our rate is close to the known rate of convergence in the usual smoothing spline model.
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The effects of large roughness elements on the in-stream transport and retention of polystyrene microplastics
Abstract The mechanisms controlling transport and retention of microplastics (MPs) in riverine systems are not understood well. We investigated the impact of large roughness elements (LREs) on in-stream transport and retention of the ubiquitous polystyrene-microplastics (PS-MPs). Scaled experiments were conducted with and without LREs under various shear Reynolds numbers (Re*) in an ecohydraulics flume. Our results, for the first time, demonstrated a clear dependence of the MPs’ velocity onRe*in LREs-dominated channel. Two distinct regimes and thresholds were identified: lowerRe*(≤ 15,000) regime corresponding to higher velocities of MPs ($${U}_{MPs}^{*}$$ > 0.45), and higherRe*(> 15,000) to lower$${U}_{MPs}^{*} ($$ < 0.45). The presence and higher density of LREs increasedRe*, decreased$${U}_{MPs}^{*}$$ , and enhanced the PS-MPs capture. The LREs-generated turbulence kinetic energy (TKE) was found to be a good predictor of PS-MPs transport and retention rates, indicating the effectiveness of LREs in retaining PS-MPs in streams and rivers.
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- PAR ID:
- 10408395
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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