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Knowledge representation learning has received a lot of attention in the past few years. The success of existing methods heavily relies on the quality of knowledge graphs. The entities with few triplets tend to be learned with less expressive power. Fortunately, there are many knowledge graphs constructed from various sources, the representations of which could contain much information. We propose an adversarial embedding transfer network ATransN, which transfers knowledge from one or more teacher knowledge graphs to a target one through an aligned entity set without explicit data leakage. Specifically, we add soft constraints on aligned entity pairs and neighbours to the existing knowledge representation learning methods. To handle the problem of possible distribution differences between teacher and target knowledge graphs, we introduce an adversarial adaption module. The discriminator of this module evaluates the degree of consistency between the embeddings of an aligned entity pair. The consistency score is then used as the weights of soft constraints. It is not necessary to acquire the relations and triplets in teacher knowledge graphs because we only utilize the entity representations. Knowledge graph completion results show that ATransN achieves better performance against baselines without transfer on three datasets, CN3l, WK3l, and DWY100k. The ablation study demonstrates that ATransN can bring steady and consistent improvement in different settings. The extension of combining other knowledge graph embedding algorithms and the extension with three teacher graphs display the promising generalization of the adversarial transfer network., Comment: Accepted by TheWebConf 2021

In this work we present the photometric and spectroscopic observations of Type IIb Supernova 2017gpn. This supernova was discovered in the error-box of LIGO/Virgo G299232 gravitational-wave event. We obtained the light curves in B and R passbands and modelled them numerically using the one-dimensional radiation hydrocode STELLA. The best-fit model has the following parameters: the pre-SN star mass and the radius are 3.5 Msun and 50 Rsun, respectively; the explosion energy is Eexp = 1.2 * 10^51 erg; the mass of radioactive nickel is M56Ni =0.11 Msun, which is totally mixed through the ejecta, the mass of the hydrogen envelope 0.06 Msun. Moreover, SN 2017gpn is a confirmed SN IIb that is located at the farthest distance from the center of its host galaxy NGC 1343 (i.e. the projected distance is about 21 kpc). This challenges the scenario of the origin of Type IIb Supernovae from massive stars., Comment: 18 pages, 13 figures

We present extensive observations of SN 2018zd covering the first $\sim450$\,d after the explosion. This SN shows a possible shock-breakout signal $\sim3.6$\,hr after the explosion in the unfiltered light curve, and prominent flash-ionisation spectral features within the first week. The unusual photospheric temperature rise (rapidly from $\sim 12,000$\,K to above 18,000\,K) within the earliest few days suggests that the ejecta were continuously heated. Both the significant temperature rise and the flash spectral features can be explained with the interaction of the SN ejecta with the massive stellar wind ($0.18^{+0.05}_{-0.10}\, \rm M_{\odot}$), which accounts for the luminous peak ($L_{\rm max} = [1.36\pm 0.63] \times 10^{43}\, \rm erg\,s^{-1}$) of SN 2018zd. The luminous peak and low expansion velocity ($v \approx 3300$ km s$^{-1}$) make SN 2018zd to be like a member of the LLEV (luminous SNe II with low expansion velocities) events originated due to circumstellar interaction. The relatively fast post-peak decline allows a classification of SN 2018zd as a transition event morphologically linking SNe~IIP and SNe~IIL. In the radioactive-decay phase, SN 2018zd experienced a significant flux drop and behaved more like a low-luminosity SN~IIP both spectroscopically and photometrically. This contrast indicates that circumstellar interaction plays a vital role in modifying the observed light curves of SNe~II. Comparing nebular-phase spectra with model predictions suggests that SN 2018zd arose from a star of $\sim 12\,\rm M_{\odot}$. Given the relatively small amount of $^{56}$Ni ($0.013 - 0.035 \rm M_{\odot}$), the massive stellar wind, and the faint X-ray radiation, the progenitor of SN 2018zd could be a massive asymptotic giant branch star which collapsed owing to electron capture., Comment: Accepted for publication in MNRAS, 20 pages, 11 figures

Context: Instrumental profile (IP) is the basic property of a spectrograph. Accurate IP characterisation is the prerequisite of accurate wavelength solution. It also facilitates new spectral acquisition methods such as the forward modeling and deconvolution. Aims: We investigate an IP modeling method for the fibre-fed echelle spectrograph with the emission lines of the ThAr lamp, and explore the method to evaluate the accuracy of IP characterisation. Methods: The backbone-residual (BR) model is put forward and tested on the fibre-fed High Resolution Spectrograph (HRS) at the Chinese Xinglong 2.16-m Telescope, which is the sum of the backbone function and the residual function. The backbone function is a bell-shaped function to describe the main component and the spatial variation of IP. The residual function, which is expressed as the cubic spline function, accounts for the difference between the bell-shaped function and the actual IP. The method of evaluating the accuracy of IP characterisation is based on the spectral reconstruction and Monte Carlo simulation. Results: The IP of HRS is characterised with the BR model, and the accuracy of the characterised IP reaches 0.006 of the peak value of the backbone function. This result demonstrates that the accurate IP characterisation has been achieved on HRS with the BR model, and the BR model is an excellent choice for accurate IP characterisation of fibre-fed echelle spectrographs., Comment: 15 pages, 14 figures. Submitted to A&A

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two SI-spreading-based algorithms, SINE and TSINE, to embed static and temporal networks, respectively. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

All stellar mass black holes have hitherto been identified by X-rays emitted by gas that is accreting onto the black hole from a companion star. These systems are all binaries with black holes below 30 M$_{\odot}$$^{1-4}$. Theory predicts, however, that X-ray emitting systems form a minority of the total population of star-black hole binaries$^{5,6}$. When the black hole is not accreting gas, it can be found through radial velocity measurements of the motion of the companion star. Here we report radial velocity measurements of a Galactic star, LB-1, which is a B-type star, taken over two years. We find that the motion of the B-star and an accompanying H$\alpha$ emission line require the presence of a dark companion with a mass of $68^{+11}_{-13}$ M$_{\odot}$, which can only be a black hole. The long orbital period of 78.9 days shows that this is a wide binary system. The gravitational wave experiments have detected similarly massive black holes$^{7,8}$, but forming such massive ones in a high-metallicity environment would be extremely challenging to current stellar evolution theories$^{9-11}$., Comment: Published in Nature on Nov 28, 2019