Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userNew sunspots and aurorae in the historical Chinese text corpus? Comments on uncritical digital search applications
58
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We review some applications of the method of electronic searching for historical observations of sunspots and aurorae in the Chinese text corpus by Hayakawa et al. etc. However, we show strong shortcomings in the digital search technique as applied by them: almost all likely true sunspot and aurora records were presented before (e.g. Xu et al. 2000), which is not mentioned in those papers; the remaining records are dubious and often refer to other phenomena, neither spots nor aurorae (this also applies to Hayakawa et al. 2017c). Most of the above publications include very few Chinese texts and translations, and their tables with abbreviated keywords do not allow the reader to consider alternative interpretations (the tables also do not specify which records mention night-time). We have compared some of their event tables with previously published catalogs and found various discrepancies. There are also intrinsic inconsistencies, misleading information (lunar phase for day-time events), and dating errors. We present Chinese texts and translations for some of their presumable new aurorae: only one can be considered a likely true aurora (AD 604 Jan); some others were selected on the sole basis of the use of the word light or rainbow. Several alleged new aurorae present observations beside the Sun during day-time. There are well-known comets among their presumable aurorae. We also discuss, (i) whether heiqi ri pang can stand for black spot(s) on one side of or beside the sun, (ii) aurora color confusion in Hayakawa et al. (2015, 2016), and (iii) whether white and unusual rainbows can be aurorae.
rate research
Read More
Dim red aurora at low magnetic latitudes is a visual and recognized manifestation of geomagnetic storms. The great low-latitude auroral displays seen throughout East Asia on 16-18 September 1770 are considered to manifest one of the greatest storms. Recently found 111 historical documents in East Asia attest that these low-latitude auroral displays were succeeding for almost 9 nights during 10-19 September 1770 in the lowest magnetic latitude areas (< 30{deg}). This suggests that the duration of the great magnetic storm is much longer than usual. Sunspot drawings from 1770 reveals the fact that sunspots area was twice as large as those observed in another great storm of 1859, which substantiates this unusual storm activities in 1770. These spots likely ejected several huge, sequential magnetic structures in short duration into interplanetary space, resulting in spectacular world-wide aurorae in mid-September 1770. These findings provide new insights about the history, duration, and effects of extreme magnetic storms that may be valuable for those who need to mitigate against extreme events.
In this article, we present the results of the surveys on sunspots and auroral candidates in Rikkokushi, Japanese Official Histories from the early 7th century to 887 to review the solar and auroral activities. In total, we found one sunspot record and 13 auroral candidates in Rikkokushi. We then examine the records of the sunspots and auroral candidates, compare the auroral candidates with the lunar phase to estimate the reliability of the auroral candidates, and compare the records of the sunspots and auroral candidates with the contemporary total solar irradiance reconstructed from radioisotope data. We also identify the locations of the observational sites to review possible equatorward expansion of auroral oval. These discussions suggest a major gap of auroral candidates from the late 7th to early 9th century, which includes the minimum number of candidates reconstructed from the radioisotope data, a similar tendency as the distributions of sunspot records in contemporary China, and a relatively high magnetic latitude with a higher potential for observing aurorae more frequently than at present.
With the development of information technology, there is an explosive growth in the number of online comment concerning news, blogs and so on. The massive comments are overloaded, and often contain some misleading and unwelcome information. Therefore, it is necessary to identify high-quality comments and filter out low-quality comments. In this work, we introduce a novel task: high-quality comment identification (HQCI), which aims to automatically assess the quality of online comments. First, we construct a news comment corpus, which consists of news, comments, and the corresponding quality label. Second, we analyze the dataset, and find the quality of comments can be measured in three aspects: informativeness, consistency, and novelty. Finally, we propose a novel multi-target text matching model, which can measure three aspects by referring to the news and surrounding comments. Experimental results show that our method can outperform various baselines by a large margin on the news dataset.
In this work we seek evidence for global torsional oscillations in alpha sunspots. We have used long time series of continuum intensity and magnetic field vector maps from the Helioseismic and Magnetic Imager (HMI) instrument on board the Solar Dynamics Observatory (SDO) spacecraft. The time series analysed here span the total disk passage of 25 isolated sunspots. We found no evidence of global long-term periodic oscillations in the azimuthal angle of the sunspot magnetic field within $sim$ 1 degree. This study could help us to understand the sunspot dynamics and its internal structure.
This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem. We constructed a large-scale Chinese language dataset, and the result shows that character-level convolutional neural network works better on Chinese corpus than its corresponding pinyin format dataset. This is the first time that character-level convolutional neural network applied to text classification problem.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
