Point Cloud Processing with Neural Networks

In this project, we explore new techniques and architectures for applying deep neural networks when the input is point cloud data. We first consider applying convolutions on regular pixel and voxel grids, using polynomials of point coordinates and Fourier transforms to get a rich feature representation for all points mapped to the same pixel or voxel. We also apply these ideas to generalize the recently proposed interpolated convolution , by learning continuous-space kernels as a combination of polynomial and Fourier basis kernels. Experiments on the ModelNet40 dataset demonstrate that our methods have superior performance over the baselines in 3D object recognition

Electro-Optical Manipulation Based on Dielectric Nanoparticles

The ability to dynamically modulate plasmon resonances or Mie resonances is crucial for practical application. Electrical tuning as one of the most efficiently active tuning methods has high switching speed and large modulation depth. Silicon as a typical high refractive index dielectric material can generate strong Mie resonances, which have shown comparable performances with plasmonic nanostructures in spectral tailoring and phase modulation. However, it is still unclear whether the optical response of single silicon nanoantenna can be electrically controlled effectively. In this chapter, we introduce two types of optoelectronic devices based on Mie resonances in silicon nanoantennas. First, we observe obvious blueshift and intensity attenuation of the plasmon-dielectric hybrid resonant peaks when applying bias voltages. Second, photoluminescence (PL) enhancement and modulation are achieved together in the WS2-Mie resonator hybrid system

Analysis of frequent trading effects of various machine learning models

In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy

The Impact of Enterprise Innovation Level on IPO Underpricing under Registration System - Based on Signaling Theory

Background: Chinese capital market has continued to develop and mature recently. And in December 2021, the Central Economic Work Conference formally proposed the full implementation of the stock issuance registration system. In this context, the phenomenon of IPO underpricing has not been effectively alleviated in the Chinese capital market.Objective: This paper aims to analyze the impact of listed companies’ characteristics and underwriters’ underwriting level on high IPO underpricing in the Chinese capital market, find the main reasons for high IPO pricing, and ultimately help enterprises alleviate IPO high underpricing.Methods: Based on incomplete adjustment theory and signal transmission theory, this paper analyzes the impact of listed companies’ innovation level (R&D), governance level (CG), and underwriters’ underwriting level on high IPO underpricing in the Chinese capital market through the RE model and robustness test. And all the data are from the CSMAR database and related enterprise annual report search.Conclusion: Based on the signal transmission theory, this paper empirically finds that the high innovation level of enterprises can replace their active underpricing issuance by transmitting high-quality signals to investors, thus alleviating the phenomenon of high IPO underpricing in China. But the level of corporate governance is difficult to send a signal to investors, and underwriters have no significant impact on IPO underpricing. Therefore, the main reason for the high IPO underpricing in China is the active underpricing of the issuer. And enterprises bear the cost of eliminating information asymmetry through low-price issuance between enterprises and investors

Neural Video Compression with Diverse Contexts

For any video codecs, the coding efficiency highly relies on whether the current signal to be encoded can find the relevant contexts from the previous reconstructed signals. Traditional codec has verified more contexts bring substantial coding gain, but in a time-consuming manner. However, for the emerging neural video codec (NVC), its contexts are still limited, leading to low compression ratio. To boost NVC, this paper proposes increasing the context diversity in both temporal and spatial dimensions. First, we guide the model to learn hierarchical quality patterns across frames, which enriches long-term and yet high-quality temporal contexts. Furthermore, to tap the potential of optical flow-based coding framework, we introduce a group-based offset diversity where the cross-group interaction is proposed for better context mining. In addition, this paper also adopts a quadtree-based partition to increase spatial context diversity when encoding the latent representation in parallel. Experiments show that our codec obtains 23.5% bitrate saving over previous SOTA NVC. Better yet, our codec has surpassed the under-developing next generation traditional codec/ECM in both RGB and YUV420 colorspaces, in terms of PSNR. The codes are at https://github.com/microsoft/DCVC.Comment: Accepted by CVPR 2023. Codes are at https://github.com/microsoft/DCV