Perturbation-based Self-supervised Attention for Attention Bias in Text Classification

In text classification, the traditional attention mechanisms usually focus too much on frequent words, and need extensive labeled data in order to learn. This paper proposes a perturbation-based self-supervised attention approach to guide attention learning without any annotation overhead. Specifically, we add as much noise as possible to all the words in the sentence without changing their semantics and predictions. We hypothesize that words that tolerate more noise are less significant, and we can use this information to refine the attention distribution. Experimental results on three text classification tasks show that our approach can significantly improve the performance of current attention-based models, and is more effective than existing self-supervised methods. We also provide a visualization analysis to verify the effectiveness of our approach

Improving Biomedical Entity Linking with Retrieval-enhanced Learning

Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models. However, existing BioEL methods usually struggle to handle rare and difficult entities due to long-tailed distribution. To address this limitation, we introduce a new scheme $k$NN-BioEL, which provides a BioEL model with the ability to reference similar instances from the entire training corpus as clues for prediction, thus improving the generalization capabilities. Moreover, we design a contrastive learning objective with dynamic hard negative sampling (DHNS) that improves the quality of the retrieved neighbors during inference. Extensive experimental results show that $k$NN-BioEL outperforms state-of-the-art baselines on several datasets.Comment: Accepted by ICASSP 202

Photometric Variability in the CSTAR Field: Results From the 2008 Data Set

The Chinese Small Telescope ARray (CSTAR) is the first telescope facility built at Dome A, Antarctica. During the 2008 observing season, the installation provided long-baseline and high-cadence photometric observations in the i-band for 18,145 targets within 20 deg2 CSTAR field around the South Celestial Pole for the purpose of monitoring the astronomical observing quality of Dome A and detecting various types of photometric variability. Using sensitive and robust detection methods, we discover 274 potential variables from this data set, 83 of which are new discoveries. We characterize most of them, providing the periods, amplitudes and classes of variability. The catalog of all these variables is presented along with the discussion of their statistical properties.Comment: 38 pages, 11 figures, 4 tables; Accepted for publication in ApJ

Eclipsing Binaries From the CSTAR Project at Dome A, Antarctica

The Chinese Small Telescope ARray (CSTAR) has observed an area around the Celestial South Pole at Dome A since 2008. About $20,000$ light curves in the i band were obtained lasting from March to July, 2008. The photometric precision achieves about 4 mmag at i = 7.5 and 20 mmag at i = 12 within a 30 s exposure time. These light curves are analyzed using Lomb--Scargle, Phase Dispersion Minimization, and Box Least Squares methods to search for periodic signals. False positives may appear as a variable signature caused by contaminating stars and the observation mode of CSTAR. Therefore the period and position of each variable candidate are checked to eliminate false positives. Eclipsing binaries are removed by visual inspection, frequency spectrum analysis and locally linear embedding technique. We identify 53 eclipsing binaries in the field of view of CSTAR, containing 24 detached binaries, 8 semi-detached binaries, 18 contact binaries, and 3 ellipsoidal variables. To derive the parameters of these binaries, we use the Eclipsing Binaries via Artificial Intelligence (EBAI) method. The primary and the secondary eclipse timing variations (ETVs) for semi-detached and contact systems are analyzed. Correlated primary and secondary ETVs confirmed by false alarm tests may indicate an unseen perturbing companion. Through ETV analysis, we identify two triple systems (CSTAR J084612.64-883342.9 and CSTAR J220502.55-895206.7). The orbital parameters of the third body in CSTAR J220502.55-895206.7 are derived using a simple dynamical model.Comment: 41 pages, 12 figures; published online in ApJ

Relation-aware Ensemble Learning for Knowledge Graph Embedding

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.Comment: This short paper has been accepted by EMNLP 202

Herbivore exclusion stabilizes alpine grassland biomass production across spatial scales

There is growing evidence that land-use management practices such as livestock grazing can strongly impact the local diversity, functioning, and stability of grassland communities. However, whether these impacts depend on environmental condition and propagate to larger spatial scales remains unclear. Using an 8-year grassland exclosure experiment conducted at nine sites in the Tibetan Plateau covering a large precipitation gradient, we found that herbivore exclusion increased the temporal stability of alpine grassland biomass production at both the local and larger (site) spatial scales. Higher local community stability was attributed to greater stability of dominant species, whereas higher stability at the larger scale was linked to higher spatial asynchrony of productivity among local communities. Additionally, sites with higher mean annual precipitation had lower dominant species stability and lower grassland stability at both the spatial scales considered. Our study provides novel evidence that livestock grazing can impair grassland stability across spatial scales and climatic gradients

Data Release of the AST3-2 Automatic Survey from Dome A, Antarctica

AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth's surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5$\sigma$ limiting magnitude in $i$-band is 17.8 mag and the light curve precision is 4 mmag for bright stars. The data release includes photometry for over 7~million stars, from which over 3,500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light curve features with stellar properties from surveys such as StarHorse.Comment: 16 pages, 20 figures, accepted for publication in MNRA

Exoplanets in the Antarctic Sky I. The first data release of AST3-II (CHESPA) and new found variables within the southern CVZ of TESS

Located at Dome A, the highest point of the Antarctic plateau, the Chinese Kunlun station is considered to be one of the best ground-based photometric sites because of its extremely cold, dry, and stable atmosphere. A target can be monitored from there for over 40 days without diurnal interruption during a polar winter. This makes Kunlun station a perfect site to search for short-period transiting exoplanets. Since 2008, an observatory has existed at Kunlun station, and three telescopes are working there. Using these telescopes, the AST3 project has been carried out over the last 6 yr with a search for transiting exoplanets as one of its key programs (CHESPA). In the austral winters of 2016 and 2017, a set of target fields in the southern continuous viewing zone (CVZ) of TESS were monitored by the AST3-II telescope. In this paper, we introduce the CHESPA and present the first data release containing photometry of 26,578 bright stars (m(i) <= 15). The best photometric precision at the optimum magnitude for the survey is around 2 mmag. To demonstrate the data quality, we also present a catalog of 221 variables with a brightness variation greater than 5 mmag from the 2016 data. Among these variables, 179 are newly identified periodic variables not listed in the AAVSO database (https://www.aavso.org/), and 67 are listed in the Candidate Target List. These variables will require careful attention to avoid false-positive signals when searching for transiting exoplanets. Dozens of new transiting exoplanet candidates will be released in a subsequent paper

Exoplanets in the Antarctic Sky. II. 116 Transiting Exoplanet Candidates Found by AST3-II (CHESPA) within the Southern CVZ of TESS

We report first results from the CHinese Exoplanet Searching Program from Antarctica (CHESPA)-a wide-field high-resolution photometric survey for transiting exoplanets carried out using telescopes of the AST3 (Antarctic Survey Telescopes times 3) project. There are now three telescopes (AST3-I, AST3-II, and CSTAR-II) operating at Dome A-the highest point on the Antarctic Plateau-in a fully automatic and remote mode to exploit the superb observing conditions of the site, and its long and uninterrupted polar nights. The search for transiting exoplanets is one of the key projects for AST3. During the austral winters of 2016 and 2017 we used the AST3-II telescope to survey a set of target fields near the southern ecliptic pole, falling within the continuous viewing zone of the TESS mission. The first data release of the 2016 data, including images, catalogs, and light curves of 26,578 bright stars (7.5 <= m(i) <= 15), was presented in Zhang et al. The best precision, as measured by the rms of the light curves at the optimum magnitude of the survey (m(i) = 10), is around 2 mmag. We detect 222 objects with plausible transit signals from these data, 116 of which are plausible transiting exoplanet candidates according to their stellar properties as given by the TESS Input Catalog, Gaia DR2, and TESS-HERMES spectroscopy. With the first data release from TESS expected in late 2018, this candidate list will be timely for improving the rejection of potential false-positives