7,134 research outputs found
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 and 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
-nucleon scattering cross-section and subsequently more
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
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