Periodic Radio Variability in NRAO 530: Phase Dispersion Minimization Analysis

In this paper, a periodicity analysis of the radio light curves of the blazar NRAO 530 at 14.5, 8.0, and 4.8 GHz is presented employing an improved Phase Dispersion Minimization (PDM) technique. The result, which shows two persistent periodic components of $\sim 6$ and $\sim 10$ years at all three frequencies, is consistent with the results obtained with the Lomb-Scargle periodogram and weighted wavelet Z-transform algorithms. The reliability of the derived periodicities is confirmed by the Monte Carlo numerical simulations which show a high statistical confidence. (Quasi-)Periodic fluctuations of the radio luminosity of NRAO 530 might be associated with the oscillations of the accretion disk triggered by hydrodynamic instabilities of the accreted flow. \keywords{methods: statistical -- galaxies: active -- galaxies: quasar: individual: NRAO 530}Comment: 8 pages, 5 figures, accepted by RA

CIF-PT: Bridging Speech and Text Representations for Spoken Language Understanding via Continuous Integrate-and-Fire Pre-Training

Speech or text representation generated by pre-trained models contains modal-specific information that could be combined for benefiting spoken language understanding (SLU) tasks. In this work, we propose a novel pre-training paradigm termed Continuous Integrate-and-Fire Pre-Training (CIF-PT). It relies on a simple but effective frame-to-token alignment: continuous integrate-and-fire (CIF) to bridge the representations between speech and text. It jointly performs speech-to-text training and language model distillation through CIF as the pre-training (PT). Evaluated on SLU benchmark SLURP dataset, CIF-PT outperforms the state-of-the-art model by 1.94% of accuracy and 2.71% of SLU-F1 on the tasks of intent classification and slot filling, respectively. We also observe the cross-modal representation extracted by CIF-PT obtains better performance than other neural interfaces for the tasks of SLU, including the dominant speech representation learned from self-supervised pre-training.Comment: Accepted by ACL 2023 Finding

Nuclear charge radii in Bayesian neural networks revisited

In this work, a refined Bayesian neural network (BNN) based approach with six inputs including the proton number, mass number, and engineered features associated with the pairing effect, shell effect, isospin effect, and ``abnormal" shape staggering effect of $^{181,183,185}$Hg, is proposed to accurately describe nuclear charge radii. The new approach is able to well describe the charge radii of atomic nuclei with $A\ge40$ and $Z\ge20$. The standard root-mean-square (rms) deviation is $0.014$ fm for both the training and validation data. In particular, the predicted charge radii of proton-rich and neutron-rich calcium isotopes are found in good agreement with data. We further demonstrate the reliability of the BNN approach by investigating the variations of the rms deviation with extrapolation distances, mass numbers, and isospin asymmetries.Comment: 16 pages, 6 figure

Influence of different processing times on the quality of Polygoni Multiflora Radix by metabolomics based on ultra high performance liquid chromatography with quadrupole timeâ ofâ flight mass spectrometry

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136757/1/jssc5378_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136757/2/jssc5378.pd