In this paper, we present a unifying account of AOMC methods and their associated statistical and computational considerations. In doing so, we pro-pose several new families of AOMC methods, and provide theoretical and empirical analysis of their performance. (Tucker et al., 2017; Grathwohl et al., 2018).

In this paper, we propose an adaptive clustering method for point cloud feature extraction—adaptive optimal means clustering (AOMC)—and apply it to point cloud deep learning network tasks.

Our method reaches demand on Modelnet10 and shapenetcore_partanno_segmentation_benchmark datasets. In terms of deep learning network optimization, it has good performance. Additionally, our method has high accuracy and low algorithm complexity. 1.Introduction.

FAW propagating along the unjacketed silica fiber at the lowest-order mode (F 11 mode) leads to an acoustically induced displacement vector (AIDV) of˜u of˜ of˜u on the fiber medium, which is...

We demonstrate for the first time a highly customizable all polarization maintaining wavelength switchable mode-locked fiber laser by utilizing thermally controlled Lyot-filter, which delivers a...

Automatically convert JPG, PNG and BMP raster images into scalable SVG vector images.

Trail Tech Vapor/Vector Top Handlebar Clamp Durable billet aluminum protective case for Trail Tech Vapor and Striker computers. Mounts using bar clamp pinch bolts. May require extra length bolts. Fits most bikes with 90-106mm bolt spacing.

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Monte Carlo sampling in vector spaces have been shown to be improved by conditioning samples to be mutually orthogonal. Exact orthogonal cou-pling of samples is computationally intensive, hence approximate methods have been of great interest. In this paper, we present a unifying per-spective of many approximate methods by con-

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