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We performed a rigorous reverberation-mapping analysis of the broad-line region (BLR) in a highly accreting (L/L_Edd= 0.74–3.4) active galactic nucleus, Markarian 142 (Mrk 142), for the first time using concurrent observations of the inner accretion disk and the BLR to determine a time lag for the Hβλ4861 emission relative to the ultraviolet (UV) continuum variations. We used continuum data taken with the Niel Gehrels Swift Observatory in theUVW2 band, and the Las Cumbres Observatory, Dan Zowada Memorial Observatory, and Liverpool Telescope in thegband, as part of the broader Mrk 142 multiwavelength monitoring campaign in 2019. We obtained new spectroscopic observations covering the Hβbroad emission line in the optical from the Gemini North Telescope and the Lijiang 2.4 m Telescope for a total of 102 epochs (over a period of 8 months) contemporaneous to the continuum data. Our primary result states a UV-to-Hβtime lag of${8.68}_{-0.72}^{+0.75}$days in Mrk 142 obtained from light-curve analysis with a Python-based running optimal average algorithm. We placed our new measurements for Mrk 142 on the optical and UV radius–luminosity relations for NGC 5548 to understand the nature of the continuum driver. The positions of Mrk 142 on the scaling relations suggest that UV is closer to the “true” driving continuum than the optical. Furthermore, we obtain$\log (M_{•}/M_{\odot })$= 6.32 ± 0.29 assuming UV as the primary driving continuum.

 

Paleostress inversion of 141 outcrop-scale faults across the eastern flank of the southern Central Range of Taiwan, where leveling and GPS data suggest a steep gradient in rock uplift rates yields two main kinematic phases of deformation. Phase 1 consists of 93 normal faults that generally dip moderately northeast, whereas phase 2 consists of 48 strike-slip faults that generally dip steeply west-northwest. Both phases record NE-trending subhorizontal extension but different orientations of principal shortening; in phase 1, the principal shortening axis is nearly vertical, whereas in phase 2, it plunges gently to moderately southeast. The northeast extension is consistent with extension directions obtained from GPS and earthquake focal mechanisms in the central part of the southern Central Range. However, these indicators of contemporary deformation also reveal more complicated states of stress along the eastern and western flanks of the range and in the deep crust southwest of the range. We interpret these more complicated stress states as reflecting the “forceful extrusion” of the southern Central Range, where the lower crust is being pinched between more rigid crustal blocks represented by the Peikang High and the Luzon Arc. In this context, the temporal progress from strike-slip to normal faulting observed in outcrops may reflect the advection of the rocks from lower to higher structural levels. The northeast extension normal faults can be interpreted as accommodating the lateral and vertical movement of the crust in the southern Central Range. Based on thermochronological data and the onset of extrusion in southwest Taiwan in the late Pleistocene, we infer that this SW extrusion process may be younger than 0.5 Ma. 

Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact applications such as data cleaning and user stitching. The state-of-the-art entity linkage pipelines mainly depend on supervised learning that requires abundant amounts of training data. However, collecting well-labeled training data becomes expensive when the data from many sources arrives incrementally over time. Moreover, the trained models can easily overfit to specific data sources, and thus fail to generalize to new sources due to significant differences in data and label distributions. To address these challenges, we present AdaMEL, a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic. In addition, AdaMEL is capable of incorporating an additional set of labeled data to more accurately integrate data sources with different attribute importance. Extensive experiments show that our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning. Besides, it is more stable in handling different sets of data sources in less runtime. 

ABSTRACT Submillimetre galaxies represent a rapid growth phase of both star formation and massive galaxies. Mapping SMGs in galaxy protoclusters provides key insights into where and how these extreme starbursts take place in connections with the assembly of the large-scale structure in the early Universe. We search for SMGs at 850 $\rm{\mu m}$ using JCMT/SCUBA-2 in two massive protoclusters at z = 2.24, BOSS1244 and BOSS1542, and detect 43 and 54 sources with S850 > 4 mJy at the 4σ level within an effective area of 264 arcmin2, respectively. We construct the intrinsic number counts and find that the abundance of SMGs is 2.0 ± 0.3 and 2.1 ± 0.2 times that of the general fields, confirming that BOSS1244 and BOSS1542 contain a higher fraction of dusty galaxies with strongly enhanced star formation. The volume densities of the SMGs are estimated to be ∼15–30 times the average, significantly higher than the overdensity factor (∼6) traced by H α emission-line galaxies (HAEs). More importantly, we discover a prominent offset between the spatial distributions of the two populations in these two protoclusters – SMGs are mostly located around the high-density regions of HAEs, and few are seen inside these regions. This finding may have revealed for the first time the occurrence of violent star formation enhancement in the outskirts of the HAE density peaks, likely driven by the boosting of gas supplies and/or starburst triggering events. Meanwhile, the lack of SMGs inside the most overdense regions at z ∼ 2 implies a transition to the environment disfavouring extreme starbursts.