What Trends in Chinese Social Media

There has been a tremendous rise in the growth of online social networks all over the world in recent times. While some networks like Twitter and Facebook have been well documented, the popular Chinese microblogging social network Sina Weibo has not been studied. In this work, we examine the key topics that trend on Sina Weibo and contrast them with our observations on Twitter. We find that there is a vast difference in the content shared in China, when compared to a global social network such as Twitter. In China, the trends are created almost entirely due to retweets of media content such as jokes, images and videos, whereas on Twitter, the trends tend to have more to do with current global events and news stories

The Leptoquark Implication from the CMS and IceCube Experiments

The recent excess in the CMS measurements of $eejj$ and $e\nu jj$ channels and the emergence of PeV comsic neutrino events at the IceCube experiment share an intriguing implication for a leptoquark with a 600-650 GeV mass. We investigate the CMS constraints on the flavor structure of a scenario with the minimal leptoquark Yukawa couplings and correlate such a scenario to the resonant enhancement in the very high energy shower event rates at the IceCube. We find for a single leptoquark, the CMS signals require large couplings to the third generation leptons. This leads to an enhancement in the $\nu_\tau$-nucleon scattering cross-section and subsequently more $\nu_\tau$ events at PeV energies. However, a visible enhancement above the Standard Model scattering would require a leptoquark Yukawa coupling larger than one that can be easily tested at the upcoming LHC runs.Comment: PRD version. Meson decay constraints and additional citations are added. 6 pages, 2 figures, 1 tabl

Fast Machine Learning Method with Vector Embedding on Orthonormal Basis and Spectral Transform

This paper presents a novel fast machine learning method that leverages two techniques: Vector Embedding on Orthonormal Basis (VEOB) and Spectral Transform (ST). The VEOB converts the original data encoding into a vector embedding with coordinates projected onto orthonormal bases. The Singular Value Decomposition (SVD) technique is used to calculate the vector basis and projection coordinates, leading to an enhanced distance measurement in the embedding space and facilitating data compression by preserving the projection vectors associated with the largest singular values. On the other hand, ST transforms sequence of vector data into spectral space. By applying the Discrete Cosine Transform (DCT) and selecting the most significant components, it streamlines the handling of lengthy vector sequences. The paper provides examples of word embedding, text chunk embedding, and image embedding, implemented in Julia language with a vector database. It also investigates unsupervised learning and supervised learning using this method, along with strategies for handling large data volumes.Comment: update 9. Strategies for managing large data volumes with 9.1. Using incremental SV