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Eclipsing binaries (EBs) play an important astrophysical role in studying stellar properties and evolution. By analyzing photometric data in the LAMOST Medium-Resolution Survey field, RA: $23^h$$01^m$$51.00^s$, Dec: +34$^\circ$36$^\prime$45$^{\prime \prime}$, 48 EBs are detected and 2 are newly discovered. This specific field has been observed 52 times by the LAMOST Medium-Resolution Survey DR 9, which facilitates a comprehensive analysis of the EBs. For EBs with LAMOST medium-resolution spectra, radial velocity curves were obtained, and their precise orbital parameters were determined by simultaneously analyzing photometric light curves and radial velocity curves. For the other EBs with only photometric light curves, we used the q-search or the temperature ratio method to determine their initial mass ratios and then determined the orbital parameters. It is found that 15 EBs belong to detached systems, 1 to semi-detached systems, and 32 to contact systems. Based on the O-C analysis for 26 EBs with sufficient eclipsing times, we found a long-term decrease in the orbital period of 11 EBs and a continuous increase of 5 EBs, which are due to the material transfer between the two components. The O-C curve of 1 EB shows a distinct periodic variation, which is caused by the light travel time effect, and the third body is likely to be a black hole. By applying the spectral subtraction method to 13 EBs with LAMOST medium-resolution spectra, 10 systems exhibit distinct H$\alpha$ emission lines, in which 1 system exhibits double-peaked lines near phases 0.25 and 0.75, implying strong chromospheric activity. In the mass-luminosities and mass-radius distributions, most of the more massive components are less evolved than the less massive ones., Comment: 19 pages, 7 figures, 6 tables, accepted by ApJ, Data available via China-VO PaperData repository

This paper presents the first analysis of the contact binary TYC 3801-1529-1. We observed four sets of multiple bands complete light curves and one set of radial velocity curve of the primary component. Based on a simultaneous investigation of our observed and TESS light curves and the radial velocity curve, we found that TYC 3801-1529-1 is an extremely low-mass-ratio, medium contact binary with $q=0.0356$, with the contribution of the third light at a level of about 10\%. Its mass ratio is lower than V1187 Her, making TYC 3801-1529-1 the lowest mass-ratio contact binary ever found in the universe. The light curves observed in 2022 are asymmetric, which is aptly explained by a hot spot on the primary component. A 16-year eclipse timings analysis indicates a secular increase orbital period with a rate of dp/dt$=7.96(\pm0.35)\times10^{-7}$ d yr$^{-1}$. We studied the stability of this target and identified that not only the value of $J_{spin}/J_{orb}$, but also the mass ratio surpass the unstable boundary. Hence, TYC 3801-1529-1 presents a challenge to theoretical research and ought to be considered a progenitor of a contact binary merger., Comment: 6 pages, 3 figures, and 1 table, accepted by A&A Letters, Data available via China-VO PaperData repository

The study of band topology in photonic crystals was primarily focused on near-field effects, including edge states and high order corner states. However, this work investigated the polarization distribution of radiated fields for photonic crystal slabs to get their far-field properties of band topology. We introduced a new topological invariant: the winding number of far-field polarization around the Brillouin zone boundary and confirmed a robust correspondence between it and the Chern number of energy bands from the perspective of symmetry, which can be used to analyze the process of topological phase transition. It is found that changes in the winding number and Chern number, associated with the exchange of far-field polarization singularities, especially for bound states in the continuum(BIC), will emerge during phase transition. These findings offer new insights for further understanding the intriguing properties of topological materials.

We give a complete classification of isoparametric hypersurfaces in a product space $M^2_{\kappa_1}\times M^2_{\kappa_2}$ of $2$-dimensional space forms for $\kappa_i\in \{-1,0,1\}$ with $\kappa_1\neq \kappa_2$. In fact we prove that any isoparametic hypersurface in such a space has constant product angle function, which enables us to remove the condition of constant principal curvatures from the classification obtained recently by J.B.M.dos Santos and J.P.dos Santos., Comment: 8 pages

The habitability of planets is closely connected with the stellar activity, mainly the frequency of flares and the distribution of flare energy. Kepler and TESS find many flaring stars are detected via precise time-domain photometric data, and the frequency and energy distribution of stellar flares on different types of stars are studied statistically. However, the completeness and observational bias of detected flare events from different missions (e.g. Kepler and TESS) vary a lot. We use a unified data processing and detection method for flares events based on the light curve from Kepler and TESS. Then we perform injection and recovery tests in the original light curve of each star for each flare event to correct the completeness and energy of flares. Three samples of flaring stars are selected from Kepler and TESS, with rotating periods from 1 to $\sim$ 5 days. Adopting a hot-blackbody assumption, our results show that the cumulative flare frequency distributions (FFDs) of the same stars in Kepler and TESS bands tend to be consistent after correction, revealing a more natural flaring frequency and energy distribution. Our results also extend the low-energy limit in cumulative FFD fitting to $10^{31.5-33}$ erg on different types of stars. For solar-type stars, the average power-law index of cumulative FFD ($\alpha_{\rm cum}$) is $-0.84$, which indicates that low-energy flares contribute less to the total flare energy. With a piecewise correlation between $\alpha_{\rm cum}$ and $T_{\rm eff}$, $\alpha_{\rm cum}$ first rises with $T_{\rm eff}$ from M2 to K1 stars, then slightly decreases for stars hotter than K1., Comment: 22 pages, 12 figures, 4 tables. Published in AJ

Photometric observations for the totally eclipsing binary system TYC 4002-2628-1, were obtained between November 2020 and November 2021. To determine the stellar atmospheric parameters, a spectral image was taken with the 2.16 m telescope at National Astronomical Observatory of China (NAOC). TYC 4002-2628-1 is a low-amplitude (about 0.15 mag for $V$ band), short-period (0.3670495 d), contact eclipsing binary with a total secondary eclipse. Intrinsic light curve variations and the reversal of the O'Connell effect are detected in the light curves, which may be due to spot activity. Based on the photometric solutions derived from the multi-band time series light curves, TYC 4002-2628-1 is an extremely low mass ratio contact binary with a mass ratio of $q\sim$ 0.0482 and a fill-out factor of $f\sim5\%$. By analyzing the $O-C$ variations, we find that its orbital period remains unchanged when BJD < 2458321 . Then the orbital period changed suddenly around BJD 2458743 and has an increasing rate of $dP/dt=1.62\times{10^{-5}}day\cdot yr^{-1}=140$ $second\cdot century^{-1}$ . If confirmed, TYC 4002-2628-1 would be the contact binary with the highest orbital period increasing rate so far. By investigating the ratio of orbital angular momentum to the spin angular momentum ( $J_{orb}$/$J_{spin}$ $<3$) , the instability mass ratio ($q_{inst}/q=1.84$) and the instability separation ($A_{inst}/A=1.35$), TYC 4002-2628-1 can be regarded as a merger candidate., Comment: 9 pages

In this paper, we study hypersurfaces in $\mathbb{H}^2\times\mathbb{H}^2$. We first classify the hypersurfaces with constant principal curvatures and constant product angle function. Then, we classify homogeneous hypersurfaces and isoparametric hypersurfaces, respectively. Finally, we classify the hypersurfaces with at most two distinct constant principal curvatures, as well as those with three constant principal curvatures under some additional conditions., Comment: Accepted for publication in SCIENCE CHINA Mathematics

Hazard and operability analysis (HAZOP) is the paradigm of industrial safety that can reveal the hazards of process from its node deviations, consequences, causes, measures and suggestions, and such hazards can be considered as hazard events (HaE). The classification research on HaE has much irreplaceable pragmatic values. In this paper, we present a novel deep learning model termed DLF through multifractal to explore HaE classification where the motivation is that HaE can be naturally regarded as a kind of time series. Specifically, first HaE is vectorized to get HaE time series by employing BERT. Then, a new multifractal analysis method termed HmF-DFA is proposed to win HaE fractal series by analyzing HaE time series. Finally, a new hierarchical gating neural network (HGNN) is designed to process HaE fractal series to accomplish the classification of HaE from three aspects: severity, possibility and risk. We take HAZOP reports of 18 processes as cases, and launch the experiments on this basis. Results demonstrate that compared with other classifiers, DLF classifier performs better under metrics of precision, recall and F1-score, especially for the severity aspect. Also, HmF-DFA and HGNN effectively promote HaE classification. Our HaE classification system can serve application incentives to experts, engineers, employees, and other enterprises. We hope our research can contribute added support to the daily practice in industrial safety., Comment: accepted by Computers In Industry

Hazards can be exposed by HAZOP as text information, and studying their classification is of great significance to the development of industrial informatics, which is conducive to safety early warning, decision support, policy evaluation, etc. However, there is no research on this important field at present. In this paper, we propose a novel model termed DLGM via deep learning for hazard classification. Specifically, first, we leverage BERT to vectorize the hazard and treat it as a type of time series (HTS). Secondly, we build a grey model FSGM(1, 1) to model it, and get the grey guidance in the sense of the structural parameters. Finally, we design a hierarchical-feature fusion neural network (HFFNN) to investigate the HTS with grey guidance (HTSGG) from three themes, where, HFFNN is a hierarchical structure with four types of modules: two feature encoders, a gating mechanism, and a deepening mechanism. We take 18 industrial processes as application cases and launch a series of experiments. The experimental results prove that DLGM has promising aptitudes for hazard classification and that FSGM(1, 1) and HFFNN are effective. We hope our research can contribute added value and support to the daily practice in industrial safety., Comment: information science

Non-collinear antiferromagnet Weyl semimetal $Mn_3Sn$ have attracted great research interest recently. Although large anomalous Hall effect, anomalous Nernst effect and magneto-optical effect have been observed in $Mn_3Sn$, most studies are based on single crystals. So far, it is still challenging to grow high quality epitaxial $Mn_3Sn$ thin films with transport and optical properties comparable to their single crystal counterparts. Here, we report the structure, magneto-optical and transport properties of epitaxial $Mn_3Sn$ thin films fabricated by pulsed laser deposition (PLD). Highly oriented $Mn_{3+x}Sn_{1-x}$ (0001) and (11$\bar2$0) epitaxial films are successfully growth on single crystalline $Al_2O_3$ and MgO substrates. Large anomalous Hall effect (AHE) up to $\left| \Delta R_H\right|$=3.02 $\mu\Omega\cdot cm$, and longitudinal magneto-optical Kerr effect (LMOKE) with $\theta_K$ = 38.1 mdeg at 633 nm wavelength are measured at 300 K temperature, which are comparable to $Mn_3Sn$ single crystals. Our work demonstrates that high quality $Mn_3Sn$ epitaxial thin films can be fabricated by PLD, paving the way for future device applications.

Entity extraction is critical in the intelligent advancement across diverse domains. Nevertheless, a challenge to its effectiveness arises from the data imbalance. This paper proposes a novel approach by viewing the issue through the quantitative information, recognizing that entities exhibit certain levels of commonality while others are scarce, which can be reflected in the quantifiable distribution of words. The Zipf's Law emerges as a well-suited adoption, and to transition from words to entities, words within the documents are classified as common and rare ones. Subsequently, sentences are classified into common and rare ones, and are further processed by text generation models accordingly. Rare entities within the generated sentences are then labeled using human-designed rules, serving as a supplement to the raw dataset, thereby mitigating the imbalance problem. The study presents a case of extracting entities from technical documents, and experimental results from two datasets prove the effectiveness of the proposed method. Furthermore, the significance of Zipf's law in driving the progress of AI is discussed, broadening the reach and coverage of Informetrics. This paper presents a successful demonstration of extending Informetrics to interface with AI through Zipf's Law., Comment: Journal of Informetrics

Deep metric learning aims to transform input data into an embedding space, where similar samples are close while dissimilar samples are far apart from each other. In practice, samples of new categories arrive incrementally, which requires the periodical augmentation of the learned model. The fine-tuning on the new categories usually leads to poor performance on the old, which is known as "catastrophic forgetting". Existing solutions either retrain the model from scratch or require the replay of old samples during the training. In this paper, a complete online deep metric learning framework is proposed based on mutual distillation for both one-task and multi-task scenarios. Different from the teacher-student framework, the proposed approach treats the old and new learning tasks with equal importance. No preference over the old or new knowledge is caused. In addition, a novel virtual feature estimation approach is proposed to recover the features assumed to be extracted by the old models. It allows the distillation between the new and the old models without the replay of old training samples or the holding of old models during the training. A comprehensive study shows the superior performance of our approach with the support of different backbones.

In this world full of uncertainty, where the fabric of existence weaves patterns of complexity, multifractal emerges as beacons of insight, illuminating them. As we delve into the realm of text mining that underpins various natural language processing applications and powers a range of intelligent services, we recognize that behind the veil of text lies a manifestation of human thought and cognition, intricately intertwined with the complexities. Building upon the foundation of perceiving text as a complex system, this study embarks on a journey to unravel the hidden treasures within, armed with the proposed multifractal method that deciphers the multifractal attributes embedded within the text landscape. This endeavor culminates in the birth of our novel model, which also harnesses the power of the proposed activation function to facilitate nonlinear information transmission within its neural network architecture. The success on experiments anchored in real-world technical reports covering the extraction of technical term and classification of hazard events, stands as a testament to our endeavors. This research venture not only expands our understanding of text mining but also opens new horizons for knowledge discovery across various domains.

Deep learning oriented named entity recognition (DNER) has gradually become the paradigm of knowledge discovery, which greatly promotes domain intelligence. However, the current activation function of DNER fails to treat gradient vanishing, no negative output or non-differentiable existence, which may impede knowledge exploration caused by the omission and incomplete representation of latent semantics. To break through the dilemma, we present a novel activation function termed KDAC. Detailly, KDAC is an aggregation function with multiple conversion modes. The backbone of the activation region is the interaction between exponent and linearity, and the both ends extend through adaptive linear divergence, which surmounts the obstacle of gradient vanishing and no negative output. Crucially, the non-differentiable points are alerted and eliminated by an approximate smoothing algorithm. KDAC has a series of brilliant properties, including nonlinear, stable near-linear transformation and derivative, as well as dynamic style, etc. We perform experiments based on BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's daily. The evaluation results show that KDAC is advanced and effective, and can provide more generalized activation to stimulate the performance of DNER. We hope that KDAC can be exploited as a promising activation function to devote itself to the construction of knowledge., Comment: Accepted by Neurocomputing

Hazard and operability analysis (HAZOP) is a remarkable representative in industrial safety engineering. However, a great storehouse of industrial safety knowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order to reuse and unlock the value of ISK and optimize HAZOP, we have developed a novel knowledge graph for industrial safety (ISKG) with HAZOP as the carrier through bridging data science and engineering design. Specifically, firstly, considering that the knowledge contained in HAZOP reports of different processes in industry is not the same, we creatively develope a general ISK standardization framework, it provides a practical scheme for integrating HAZOP reports from various processes and uniformly representing the ISK with diverse expressions. Secondly, we conceive a novel and reliable information extraction model based on deep learning combined with data science, it can effectively mine ISK from HAZOP reports, which alleviates the obstacle of ISK extraction caused by the particularity of HAZOP text. Finally, we build ISK triples and store them in the Neo4j graph database. We take indirect coal liquefaction process as a case study to develop ISKG, and its oriented applications can optimize HAZOP and mine the potential of ISK, which is of great significance to improve the security of the system and enhance prevention awareness for people. ISKG containing the ISK standardization framework and the information extraction model sets an example of the interaction between data science and engineering design, which can enlighten other researchers and extend the perspectives of industrial safety., Comment: accepted by Computers in industry

The cut-off mass ratio is under debate for contact binaries. In this paper, we present the investigation of two contact binaries with mass ratios close to the low mass ratio limit. It is found that the mass ratios of VSX J082700.8+462850 (hereafter J082700) and 1SWASP J132829.37+555246.1 (hereafter J132829) are both less than 0.1 ($q\sim0.055$ for J082700, and $q\sim0.089$ for J132829). J082700 is a shallow contact binary with a contact degree of $\sim$19%, and J132829 is a deep contact system with a fillout factor of $\sim$70%. The $O-C$ diagram analysis indicated that both the two systems manifest long-term period decrease. In addition, J082700 exhibits a cyclic modulation which is more likely resulted from Applegate mechanism. In order to explore the properties of extremely low mass ratio contact binaries (ELMRCBs), we carried out a statistical analysis on contact binaries with mass ratios of $q\lesssim0.1$ and discovered that the values of $J_{spin}/J_{orb}$ of three systems are greater than 1/3. Two possible explanations can interpret this phenomenon. One is that some physical processes, unknown to date, are not considered when Hut presented the dynamically instability criterion. The other is that the dimensionless gyration radius ($k$) should be smaller than the value we used ($k^2=0.06$). We also found that the formation of ELMRCBs possibly has two channels. The study of evolutionary states of ELMRCBs reveals that their evolutionary states are similar with those of normal W UMa contact binaries., Comment: 26 pages, 9 figures, and 4 tables, accepted by ApJ

During October 2019 and March 2020, the luminous red supergiant Betelgeuse demonstrated an unusually deep minimum of its brightness. It became fainter by more than one magnitude and this is the most significant dimming observed in the recent decades. While the reason for the dimming is debated, pre-phase of supernova explosion, obscuring dust, or changes in the photosphere of the star were suggested scenarios. Here, we present spectroscopic studies of Betelgeuse using high-resolution and high signal-to- noise ratio near-infrared spectra obtained at Weihai Observatory on four epochs in 2020 covering the phases of during and after dimming. We show that the dimming episode is caused by the dropping of its effective temperature by at least 170 K on 2020 January 31, that can be attributed to the emergence of a large dark spot on the surface of the star.