Efficient Compression Of Molecular Line Lists: Application Of `super-energies' To The Exomol Database

%\begin{figure} %\includegraphics[scale=0.5]{KOH_T3000K_SL_SE} %\caption{KOH absorption cross sections at $T = 3000$~K, the weak part before and after compression and the relative error. } %\label{f:KOH} %\end{figure} A new compression algorithm for the efficient storage of molecular line lists has been recently presented\footnote{R.~J. Hargreaves, I.~E. Gordon, M.~Rey, A.~V. Nikitin, V.~G. Tyuterev, R.~V. Kochanov, L.~S. Rothman, \emph{Astrophys. J. Suppl.}, 2020, \textbf{247}, 55.}. The algorithm is based on the effective `super-energies' developed to produce a compact HITEMP line list for methane. This method assumes a set of artificial lower state (super-)energies and corresponding reference intensities for an approximate description of the temperature dependent molecular absorption (absorption coefficient) on a grid of wavenumbers. The super-energies compression is applied only to the majority ($>99$\%) of the lines representing the weaker, continuum part of the molecular absorption, while the strongest lines ($<1$\%) are preserved in the original form to maintain the accuracy of the line list. Here we adopt and develop the HITEMP compression algorithm to be applicable to the ExoMol data format and generate new compressed line lists for SiO$_2$,\footnote{A.~Owens, E.~K. Conway, J.~Tennyson, S.~N. Yurchenko, \emph{Mon. Not. R. Astron. Soc.}, 2020, \textbf{495}, 1927--1933.} H$_2$O,\footnote{O.~L. Polyansky, A.~A. Kyuberis, N.~F. Zobov, J.~Tennyson, S.~N. Yurchenko, L.~Lodi, \emph{Mon. Not. R. Astron. Soc.}, 2018, \textbf{480}, 2597--2608.} KOH and NaOH.\footnote{A.~Owens, J.~Tennyson, S.~N. Yurchenko, \emph{Mon. Not. R. Astron. Soc.}, 2021, \textbf{502}, 1128--1135.} We find that using artificial Einstein A coefficients instead of reference intensities provides a more accurate description of the temperature dependence. A typical compression of a line list consisting of, e.g., 40 billions SiO$_2$ lines is to about {40} million data points. Advantages and limitations of the `super-energies' approach will be discussed. The compressed molecular line lists will be included in the ExoMol database (\textsc{www.exomol.com}) and their use should greatly facilitate atmospheric retrievals in exoplanets and other hot astronomical bodies. %\begin{wrapfigure}{b}{0pt} %\includegraphics[scale=0.4]{KOH_T3000K_SL_SE_total.eps} %\end{wrapfigure

A Survey of Cross-Lingual Sentiment Analysis Based on Pre-Trained Models

With the technology development of natural language processing, many researchers have studied Machine Learning (ML), Deep Learning (DL), monolingual Sentiment Analysis (SA) widely. However, there is not much work on Cross-Lingual SA (CLSA), although it is beneficial when dealing with low resource languages (e.g., Tamil, Malayalam, Hindi, and Arabic). This paper surveys the main challenges and issues of CLSA based on some pre-trained language models and mentions the leading methods to cope with CLSA. In particular, we compare and analyze their pros and cons. Moreover, we summarize the valuable cross-lingual resources and point out the main problems researchers need to solve in the future

Multimodal Sentiment Analysis Based on Deep Learning: Recent Progress

Multimodal sentiment analysis is an important research topic in the field of NLP, aiming to analyze speakers\u27 sentiment tendencies through features extracted from textual, visual, and acoustic modalities. Its main methods are based on machine learning and deep learning. Machine learning-based methods rely heavily on labeled data. But deep learning-based methods can overcome this shortcoming and capture the in-depth semantic information and modal characteristics of the data, as well as the interactive information between multimodal data. In this paper, we survey the deep learning-based methods, including fusion of text and image and fusion of text, image, audio, and video. Specifically, we discuss the main problems of these methods and the future directions. Finally, we review the work of multimodal sentiment analysis in conversation