A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding - Part I: Geometry.

A universal multiscale conditional coding framework, Unicorn, is proposed to compress the geometry and attribute of any given point cloud. Geometry compression is addressed in Part I of this paper, while attribute compression is discussed in Part II. We construct the multiscale sparse tensors of each voxelized point cloud frame and properly leverage lower-scale priors in the current and (previously processed) temporal reference frames to improve the conditional probability approximation or content-aware predictive reconstruction of geometry occupancy in compression.

A Versatile Point Cloud Compressor Using Universal Multiscale Conditional Coding - Part II: Attribute.

A universal multiscale conditional coding framework, Unicorn, is proposed to code the geometry and attribute of any given point cloud. Attribute compression is discussed in Part II of this paper, while geometry compression is given in Part I of this paper. We first construct the multiscale sparse tensors of each voxelized point cloud attribute frame.

Intracavity frequency doubling acousto-optic Q-switched high repetition rate high-energy Nd:YLF laser.

The ongoing advancement of Ti:sapphire femtosecond laser technology has drawn increasing attention to high repetition rate, high-energy green lasers as ideal pump sources for Ti:sapphire regenerative amplifiers. This study employed a neodymium-doped yttrium lithium fluoride (Nd:YLF) as the gain medium, supplemented with side-pumped laser diodes, acousto-optic -switching, and intracavity frequency doubling technologies. The results demonstrated a repetition rate ranging from 1-10 kHz, a pulse width of less than 100 ns, and a single pulse energy exceeding 50 mJ at 527 nm green light output.

GRNet:Geometry Restoration for G-PCC Compressed Point Clouds Using Auxiliary Density Signaling.

The lossy Geometry-based Point Cloud Compression (G-PCC) inevitably impairs the geometry information of point clouds, which deteriorates the quality of experience (QoE) in reconstruction and/or misleads decisions in tasks such as classification. To tackle it, this work proposes GRNet for the geometry restoration of G-PCC compressed large-scale point clouds. By analyzing the content characteristics of original and G-PCC compressed point clouds, we attribute the G-PCC distortion to two key factors: point vanishing and point displacement.

Research on Earnings Management of Growth Enterprise Market in China Stock Market: Comparative Analysis Based on the BPNN, GBDT, and MLR Models.

This study, based on 2011-2020 China's listed companies on GEM as research samples, introduces the BPNN (BP neural network) and GBDT (Gradient Boosting Decision Tree) model into the research of the relationship between internal governance and earnings management, which will be comparatively analyzed with the empirical results of the traditional multiple linear regression model, so as to study its validity and predictive power in the earnings' management research field. The results show the following. (1) The matching effect of the multiple linear regression model is poor in the analysis of GEM, with a high rate of experimental data distortion.