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We present the results of a four-year velocity-resolved reverberation mapping (RM) campaign of the changing-look active galactic nucleus (CL-AGN) NGC 4151 during its outburst phase. By measuring the time lags of the \ha, \hb, \hg, \hei, and \heii\ emission lines, we confirm a stratified broad-line region (BLR) structure that aligns with predictions from photoionization models. Intriguingly, we observed an ``anti-breathing" phenomenon, where the lags of broad emission lines decreased with increasing luminosity, contrary to the typical expectation. This anomaly may be attributed to the influence of the ultraviolet-optical lag or non-virialized motions in the BLR gas. Velocity-resolved RM and ionization mapping analyses revealed rapid and significant changes in the BLR geometry and kinematics on timescales within one year, which cannot be interpreted by any single mechanism, such as an inhomogeneous BLR, variations in radiation pressure, or changes in the illuminated ionizing field. Additionally, the \hb\ lags of NGC 4151 and other CL-AGNs agree with the radius-luminosity relationship established for AGNs with low accretion rates, implying that the CL phenomenon is more likely driven by intrinsic changes in the accretion rate rather than obscuration. These findings provide new insights into the complex internal processes of CL-AGNs and highlight the importance of long-term, multi-line RM for understanding BLR structures, geometry, and kinematics., Comment: 15 pages, 7 figures, 3 tables, Comments welcome! Submitted to ApJ

Network slicing enables industrial Internet of Things (IIoT) networks with multiservice and differentiated resource requirements to meet increasing demands through efficient use and management of network resources. Typically, the network slice orchestrator relies on demand forecasts for each slice to make informed decisions and maximize resource utilization. The new generation of Industry 4.0 has introduced digital twins to map physical systems to digital models for accurate decision-making. In our approach, we first use graph-attention networks to build a digital twin environment for network slices, enabling real-time traffic analysis, monitoring, and demand forecasting. Based on these predictions, we formulate the resource allocation problem as a federated multi-agent reinforcement learning problem and employ a deep deterministic policy gradient to determine the resource allocation policy while preserving the privacy of the slices. Our results demonstrate that the proposed approaches can improve the accuracy of demand prediction for network slices and reduce the communication overhead of dynamic network slicing., Comment: 8 pages, 7 figures, conference

This work reports the thermal conductivity of RbV3Sb5 and CsV3Sb5 with three-dimensional charge density wave phase transitions from 80 K to 400 K measured by pump-probe thermoreflectance techniques. The in-plane (basal plane) thermal conductivities are found moderate, i.e., 12 W/mK of RbV3Sb5 and 8.8 W/mK of CsV3Sb5 at 300 K. Low cross-plane (stacking direction) thermal conductivities are observed, with 0.72 W/mK of RbV3Sb5 and 0.49 W/mK of CsV3Sb5 at 300 K. A unique glass-like temperature dependence in the cross-plane thermal conductivity is observed, which decreases monotonically even lower than the Cahill-Pohl limit as the temperature decreases below the phase transition point TCDW. This temperature dependence is found to obey the hopping transport picture. In addition, a peak in cross-plane thermal conductivity is observed at TCDW as a fingerprint of the modulated structural distortion along the stacking direction.

Thermo-optic (TO) phase shifters are very fundamental units in large-scale active silicon photonic integrated circuits (PICs). However, due to the limitation of microheater materials with a trade-off between heating efficiency and absorption loss, designs reported so far typically suffer from slow response time, high power consumption, low yields, and so on. Here, we demonstrate an energy-efficient, fast-response, and low-loss TO phase shifter by introducing hydrogen-doped indium oxide (IHO) films as microheater, and the optimized electron concentration with enhanced mobility endows the IHO high conductivity as well as high near-infrared (NIR) transparency, which allow it to directly contact the silicon waveguide without any insulating layer for efficient tuning and fast response. The TO phase shifter achieves a sub-microsecond response time (970 ns/980 ns) with a {\pi} phase shift power consumption of 9.6 mW. And the insertion loss introduced by the IHO microheater is ~ 0.5 dB. The proposed IHO-based microheaters with compatible processing technology illustrate the great potential of such material in the application of large-scale silicon PICs., Comment: 10 pages, 4 figures, journal

We report the fourth installment in the series of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey, which includes quasars observed between September, 2017 and June, 2021. There are in total 13,066 quasars reliably identified, of which 6,685 are newly discovered that are not reported in the SDSS DR14 quasar catalog or Million Quasars catalog. Because LAMOST does not provide accurate absolute flux calibration, we re-calibrate the spectra with the SDSS/Pan-STARRS1 multi-band photometric data. The emission line properties of H$\alpha$, H$\beta$, Mg\,{\sc ii} and C\,{\sc iv}, and the continuum luminosities are measured by fitting the re-calibrated spectra. We also estimate the single-epoch virial black hole masses ($\rm M_{BH}$) using the derived emission line and continuum parameters. This is the first time that the emission line and continuum fluxes were estimated based on LAMOST re-calibrated quasar spectra. The catalog and spectra for these quasars are available online. After the nine-year LAMOST quasar survey, there are in total 56,175 identified quasars, of which 24,127 are newly discovered. The LAMOST quasar survey not only discovers a great number of new quasars, but also provides a database for investigating the spectral variability of the quasars observed by both LAMOST and SDSS, and finding rare quasars including changing-look quasars and broad absorption line quasars., Comment: arXiv admin note: text overlap with arXiv:1811.01570, Accepted by ApJS

Characterizing materials with spatially varying thermal conductivities is significant to unveil the structure-property relation for a wide range of functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal conductivity microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of thermal conductivity profile, measuring depth-dependent thermal conductivity remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K(z) directly from frequency-domain phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression (KRR) can reconstruct K(z) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K(z) distributions such as the pre-assumed exponential profile of chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated materials, but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition to FDTR, the method also shows excellent performances of reconstructing K(z) of ion-irradiated semiconductors from TDTR signals. This work demonstrates that combining machine learning with pump-probe thermoreflectance is an effective way for depth-dependent thermal property mapping.